accim: a Python library to perform building energy simulations considering adaptive comfort and data analytics

This Jupyter Notebook contains the code for the case study (Section 3 Results) of the research paper accim: a Python library to perform building energy simulations considering adaptive comfort and data analytics. It has been developed to ease the reproducibility of the paper, and the replicability with other IDF and EPW files or other arguments.

3. Results

In this case study, the aim is the analysis of energy demand resulting from the use of adaptive setpoint temperatures based on a local adaptive comfort model for office buildings in India, named IMAC-C, and the comparison with ASHRAE 55 adaptive model and static setpoints from the Indian Building Code, in different operation modes. In this study, 2 different locations, a present and a future scenario, are considered, resulting in 4 different EPW files.

The building energy model used is the 2018 IECC DOE Commercial Reference Building Prototype Small Office. It has 6 thermal zones: a thermal zone in the centre of the building (CORE_ZN), surrounded by 4 thermal zones (PERIMETER_ZN_1 to 4), all of them air-conditioned and located on the ground floor, and a non-air-conditioned attic (Figure 3). Since this is a well-known model, no further details are provided, however these are included in the folder with the IDF and EPW files, in an XLS file provided by DOE and named “PNNL_Scorecard_Prototypes_Office_Small.xls” as well as available online in https://www.energycodes.gov/prototype-building-models#Commercial.

The available EPW files in this path are:

[1]:

from os import listdir
input_files = [i for i in listdir()]
input_epws = [i for i in input_files if i.endswith('.epw')]
print(*input_epws, sep='\n')

Current_Ahmedabad-hour.epw
Current_Shimla-hour.epw
RCP852100_Ahmedabad-hour.epw
RCP852100_Shimla-hour.epw

and the available IDF file or files are:

[2]:

input_idfs = [i for i in input_files if i.endswith('.idf')]
print(*input_idfs, sep='\n')

SmallOffice.idf

At this point, the methodology explained in the research paper can be applied. It will be composed of the following sections and subsections:

  • Data pre-processing

    • EPW files preparation

    • ACCIS implementation

  • Running simulations

  • Data analysis

    • Tables

    • Data visualization

3.1 Data pre-processing

At this stage, input files need to be prepared for simulation.

3.1.1. EPW files preparation

First, EPWs will be formatted to a suitable name pattern for later simulation. To do so, the user can instantiate the class rename_epw_files. After instantiating the class, the user will need to interact with accim at the python console or CMD terminal. confirm_deletion argument has been set to False to avoid deletion of the original files, since these are needed to re-run the Jupyter Notebook

[3]:

from accim.data.data_preprocessing import rename_epw_files
rename_epw_files(
    rename_dict={
        'Ahmedabad': 'Ahmedabad',
        'Shimla': 'Shimla'
    },
    confirm_deletion=False,
    confirm_renaming=True
)

Since no match has been found between RCP or SSP scenario Year and EPW file name, Present year has been assigned to the following EPW files:
Current_Ahmedabad-hour.epw
Current_Shimla-hour.epw
The geolocation process has taken: 1.74 seconds (0.43 s/EPW)

The previous and new names of the EPW files and their unique IDs are:
ID: 0 / Current_Ahmedabad-hour / India_Ahmedabad_Present
ID: 1 / Current_Shimla-hour / India_Shimla_Present
ID: 2 / RCP852100_Ahmedabad-hour / India_Ahmedabad_RCP85-2100
ID: 3 / RCP852100_Shimla-hour / India_Shimla_RCP85-2100


If any of the city or subcountry names needs some amendment (if you are not happy with any of the available options, you can exclude it from renaming at the next stage), please enter the EPW IDs separated by space; otherwise, hit enter to omit:


The final list of previous and new names of the EPW files and their unique IDs is:
ID: 0 / Current_Ahmedabad-hour / India_Ahmedabad_Present
ID: 1 / Current_Shimla-hour / India_Shimla_Present
ID: 2 / RCP852100_Ahmedabad-hour / India_Ahmedabad_RCP85-2100
ID: 3 / RCP852100_Shimla-hour / India_Shimla_RCP85-2100


If you want to exclude some EPWs from renaming, please enter the IDs separated by space, otherwise, hit enter to continue:

The file Current_Ahmedabad-hour has been renamed to India_Ahmedabad_Present
The file Current_Shimla-hour has been renamed to India_Shimla_Present
The file RCP852100_Ahmedabad-hour has been renamed to India_Ahmedabad_RCP85-2100
The file RCP852100_Shimla-hour has been renamed to India_Shimla_RCP85-2100

[3]:

<accim.data.data_preprocessing.rename_epw_files at 0x15e0df97a60>

The tool renamed correctly all EPW files at the first instance and no amendments were required. The new EPWs are:

[4]:

output_epws = [i for i in listdir() if i.endswith('.epw') if i not in input_epws]
print(*output_epws, sep='\n')

India_Ahmedabad_Present.epw
India_Ahmedabad_RCP85-2100.epw
India_Shimla_Present.epw
India_Shimla_RCP85-2100.epw

Now, the old EPWs need to be moved to a different folder to save them as a backup.

[5]:

import shutil
for i in input_epws:
    shutil.move(i, f'backup/{i}')

3.1.2. ACCIS implementation

First, let’s apply 24/7 (always on) schedules to occupancy, so that we have the HVAC system on all the hours of the year.

[6]:

from accim.utils import set_occupancy_to_always_path
set_occupancy_to_always_path(idfpath='SmallOffice.idf')

On 24/7 Schedule:Compact object was already in the model.
People Block1:PerimeterXZNX4 Number of People Schedule Name has been set to always occupied.
People Block1:CoreXZN Number of People Schedule Name has been set to always occupied.
People Block1:PerimeterXZNX2 Number of People Schedule Name has been set to always occupied.
People Block1:PerimeterXZNX1 Number of People Schedule Name has been set to always occupied.
People Block1:PerimeterXZNX3 Number of People Schedule Name has been set to always occupied.

The next step is applying the adaptive setpoint temperatures. To do so, the function addAccis needs to be used. Again, the recommended process, suitable for users with no programming background, is:

  • open a CMD terminal pointing at the path where the EPW files to be renamed are located

  • execute Python by entering py or python

  • import the package by entering from accim.sim import accis

  • call the function by entering accis.addAccis()

  • enter the required information on CMD terminal (related to the arguments that have not been specified)

In this case study, all arguments are specified as shown below:

  • ScriptType is 'vrf_mm', since this option adds a generic VRF system for each zone, and the input IDF was a generic model exported from DesignBuilder without any HVAC system at all.

  • 'temperature difference' in argument SupplyAirTempInputMethod specifies the type of supply air temperature input method for the VRF systems. This is due to the fact that, running simulations in hot weather with high cooling setpoint temperatures might result in little cooling needs, sometimes too little to be used with a generic cooling supply air temperature of 14°C.

  • Output_keep_existing is set to False in order to not to generate unnecessary results.

  • Output_type is set to 'standard', in order to provide typical results. Other options might be used when a large amount of simulations is carried out and file size needs to be reduced, or when very specific results need to be exported for the testing of the tool.

  • Output_freqs is set only to 'hourly', since aggregation into other frequencies will be performed later in data analysis. If additional frequencies were required, these could be added to the list (i.e. ['hourly', 'daily', 'monthly', 'runperiod'])

  • Output_gen_dataframe is set to False, since the generation of the panmdas DataFrame containing Output:Variable objects for output customisation is not needed.

  • EnergyPlus_version is set to '23.1', since that is the version of TestModel.idf.

  • TempCtrl is set to 'temperature', so that the operation of the HVAC system is based on the operative temperature. If 'pmv' was used, setpoints would be ±0.5.

  • ComfStand requested values are 2 and 7, since these are the numbers respectively for ASHRAE 55 and IMAC-C in NV mode.

  • CAT is set only to 80, so that the offset of setpoint temperatures from neutral temperature covers an 80% acceptability.

  • ComfMod requested values are 0 and 3, since these are respectively for static and adaptive setpoints, and in this study, adaptive setpoints from IMAC-C and static setpoints from Indian Building Code are to be compared.

  • SetpointAcc is set to 100, since HVAC units thermostats are considered to be accurate up to the second decimal.

  • CoolSeasonStart and CoolSeasonEnd are set to dates 01/05 and 01/10 respectively, however these are not necessary in this case.

  • HVACmode requested values are 0, 1 and 2, since those are respectively for AC, NV and MM operation modes. In this case study, may be interesting to evaluate the energy performance of MM against AC. Also, may be interesting to see the difference in comfort hours between NV and MM mode.

  • VentCtrl, VSToffset, MinOToffset and MaxWindSpeed are arguments to control the operation of MM. In this case, the windows are opened when operative temperature exceeds the neutral temperature (VentCtrl is 0) with no offset (VSToffset is 0), and no restrictions in terms of minimum outdoor temperature and maximum wind speed are set (MinOToffset and MaxWindSpeed are 50).

  • ASTtol_steps, ASTtol_start and ASTtol_end_input are mainly used to apply some tolerances to make sure there are no hours that fall outside the comfort zone by a little extent. These can also be used to generate an array of setpoint temperatures. In this case, it applies a ±0.1°C offset to heating and cooling setpoints.

[7]:

from accim.sim import accis
accis.addAccis(
    ScriptType='vrf_mm',
    SupplyAirTempInputMethod='temperature difference',
    Output_keep_existing=False,
    Output_type='standard',
    Output_freqs=['hourly'],
    Output_gen_dataframe=False,
    EnergyPlus_version='23.1',
    TempCtrl='temperature',
    ComfStand=[2, 7],
    CAT=[80],
    ComfMod=[0, 3],
    SetpointAcc=100,
    CoolSeasonStart='01/05',
    CoolSeasonEnd='01/10',
    HVACmode=[0, 1, 2],
    VentCtrl=[0],
    VSToffset=[0],
    MinOToffset=[50],
    MaxWindSpeed=[50],
    ASTtol_steps=0.1,
    ASTtol_start=0.1,
    ASTtol_end_input=0.1,
    confirmGen=True,
)


--------------------------------------------------------
Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.3
--------------------------------------------------------

This tool allows to apply adaptive setpoint temperatures.
For further information, please read the documentation:
https://accim.readthedocs.io/en/master/
For a visual understanding of the tool, please visit the following jupyter notebooks:
-    Using addAccis() to apply adaptive setpoint temperatures
https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html
-    Using rename_epw_files() to rename the EPWs for proper data analysis after simulation
https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html
-    Using runEp() to directly run simulations with EnergyPlus
https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html
-    Using the class Table() for data analysis
https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html
-    Full example
https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html

Starting with the process.
Basic input data:
ScriptType is: vrf_mm
Supply Air Temperature Input Method is: temperature difference
Output type is: standard
Output frequencies are:
['hourly']
EnergyPlus version is: 23.1
Temperature Control method is: temperature

=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================

Starting with file:
SmallOffice
IDD location is: C:/EnergyPlusV23-1-0/Energy+.idd
The occupied zones in the model SmallOffice are:
BLOCK1:PERIMETERXZNX4
BLOCK1:COREXZN
BLOCK1:PERIMETERXZNX2
BLOCK1:PERIMETERXZNX1
BLOCK1:PERIMETERXZNX3
The windows and doors in the model SmallOffice are:
Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win
Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win
Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win
Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win
Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win
Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win
Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win
Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win
Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win
Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win
Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win
Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win
Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win
Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win
Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win
Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door
Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door
Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win
Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win
Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win
Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win
Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win
Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win
The zones in the model SmallOffice are:
BLOCK1_PERIMETERXZNX4
BLOCK1_COREXZN
BLOCK1_PERIMETERXZNX2
BLOCK1_PERIMETERXZNX1
BLOCK1_PERIMETERXZNX3
The people objects in the model have been amended.
BLOCK1:PERIMETERXZNX4 Thermostat has been added
BLOCK1:COREXZN Thermostat has been added
BLOCK1:PERIMETERXZNX2 Thermostat has been added
BLOCK1:PERIMETERXZNX1 Thermostat has been added
BLOCK1:PERIMETERXZNX3 Thermostat has been added
On Schedule already was in the model
TypOperativeTempControlSch Schedule already was in the model
All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on
On 24/7 Schedule already was in the model
Control type schedule: Always 4 Schedule has been added
Relative humidity setpoint schedule: Always 50.00 Schedule has been added
Heating Fanger comfort setpoint: Always -0.5 Schedule has been added
Cooling Fanger comfort setpoint: Always  0.1 Schedule has been added
Zone CO2 setpoint: Always 900ppm Schedule has been added
Min CO2 concentration: Always 600ppm Schedule has been added
Generic contaminant setpoint: Always 0.5ppm Schedule has been added
Air distribution effectiveness (always 1) Schedule has been added
VRF Heating Cooling (Northern Hemisphere) Schedule has been added
DefaultFanEffRatioCurve Curve:Cubic Object has been added
VRFTUCoolCapFT Curve:Cubic Object has been added
VRFTUHeatCapFT Curve:Cubic Object has been added
VRFCoolCapFTBoundary Curve:Cubic Object has been added
VRFCoolEIRFTBoundary Curve:Cubic Object has been added
CoolingEIRLowPLR Curve:Cubic Object has been added
VRFHeatCapFTBoundary Curve:Cubic Object has been added
VRFHeatEIRFTBoundary Curve:Cubic Object has been added
HeatingEIRLowPLR Curve:Cubic Object has been added
DefaultFanPowerRatioCurve Curve:Exponent Object has been added
DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added
VRFCoolCapFT Curve:Biquadratic Object has been added
VRFCoolCapFTHi Curve:Biquadratic Object has been added
VRFCoolEIRFT Curve:Biquadratic Object has been added
VRFCoolEIRFTHi Curve:Biquadratic Object has been added
VRFHeatCapFT Curve:Biquadratic Object has been added
VRFHeatCapFTHi Curve:Biquadratic Object has been added
VRFHeatEIRFT Curve:Biquadratic Object has been added
VRFHeatEIRFTHi Curve:Biquadratic Object has been added
CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added
VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added
VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added
VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added
VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added
VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added
VRFACCoolCapFFF Curve:Quadratic Object has been added
CoolingEIRHiPLR Curve:Quadratic Object has been added
VRFCPLFFPLR Curve:Quadratic Object has been added
HeatingEIRHiPLR Curve:Quadratic Object has been added
CoolingCombRatio Curve:Linear Object has been added
HeatingCombRatio Curve:Linear Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX4 AirConditioner:VariableRefrigerantFlow Object has been added
VRF Outdoor Unit_BLOCK1:COREXZN AirConditioner:VariableRefrigerantFlow Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX2 AirConditioner:VariableRefrigerantFlow Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX1 AirConditioner:VariableRefrigerantFlow Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX3 AirConditioner:VariableRefrigerantFlow Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX4 Outdoor Air Node Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX4 Zone List Object has been added
VRF Outdoor Unit_BLOCK1:COREXZN Outdoor Air Node Object has been added
VRF Outdoor Unit_BLOCK1:COREXZN Zone List Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX2 Outdoor Air Node Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX2 Zone List Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX1 Outdoor Air Node Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX1 Zone List Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX3 Outdoor Air Node Object has been added
VRF Outdoor Unit_BLOCK1:PERIMETERXZNX3 Zone List Object has been added
BLOCK1:PERIMETERXZNX4 Sizing:Zone Object has been added
BLOCK1:COREXZN Sizing:Zone Object has been added
BLOCK1:PERIMETERXZNX2 Sizing:Zone Object has been added
BLOCK1:PERIMETERXZNX1 Sizing:Zone Object has been added
BLOCK1:PERIMETERXZNX3 Sizing:Zone Object has been added
BLOCK1:PERIMETERXZNX4 Design Specification Outdoor Air Object has been added
BLOCK1:COREXZN Design Specification Outdoor Air Object has been added
BLOCK1:PERIMETERXZNX2 Design Specification Outdoor Air Object has been added
BLOCK1:PERIMETERXZNX1 Design Specification Outdoor Air Object has been added
BLOCK1:PERIMETERXZNX3 Design Specification Outdoor Air Object has been added
BLOCK1:PERIMETERXZNX4 Design Specification Zone Air Distribution Object has been added
BLOCK1:COREXZN Design Specification Zone Air Distribution Object has been added
BLOCK1:PERIMETERXZNX2 Design Specification Zone Air Distribution Object has been added
BLOCK1:PERIMETERXZNX1 Design Specification Zone Air Distribution Object has been added
BLOCK1:PERIMETERXZNX3 Design Specification Zone Air Distribution Object has been added
BLOCK1:PERIMETERXZNX4 Nodelist Objects has been added
BLOCK1:COREXZN Nodelist Objects has been added
BLOCK1:PERIMETERXZNX2 Nodelist Objects has been added
BLOCK1:PERIMETERXZNX1 Nodelist Objects has been added
BLOCK1:PERIMETERXZNX3 Nodelist Objects has been added
BLOCK1:PERIMETERXZNX4 ZoneHVAC:EquipmentConnections Objects has been added
BLOCK1:COREXZN ZoneHVAC:EquipmentConnections Objects has been added
BLOCK1:PERIMETERXZNX2 ZoneHVAC:EquipmentConnections Objects has been added
BLOCK1:PERIMETERXZNX1 ZoneHVAC:EquipmentConnections Objects has been added
BLOCK1:PERIMETERXZNX3 ZoneHVAC:EquipmentConnections Objects has been added
BLOCK1:PERIMETERXZNX4 ZoneHVAC:EquipmentList Objects has been added
BLOCK1:COREXZN ZoneHVAC:EquipmentList Objects has been added
BLOCK1:PERIMETERXZNX2 ZoneHVAC:EquipmentList Objects has been added
BLOCK1:PERIMETERXZNX1 ZoneHVAC:EquipmentList Objects has been added
BLOCK1:PERIMETERXZNX3 ZoneHVAC:EquipmentList Objects has been added
BLOCK1:PERIMETERXZNX4 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added
BLOCK1:COREXZN ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX3 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX4 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added
BLOCK1:COREXZN Coil:Cooling:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX3 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX4 Coil:Heating:DX:VariableRefrigerantFlow Object has been added
BLOCK1:COREXZN Coil:Heating:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX3 Coil:Heating:DX:VariableRefrigerantFlow Object has been added
BLOCK1:PERIMETERXZNX4 Fan:ConstantVolume Object has been added
BLOCK1:COREXZN Fan:ConstantVolume Object has been added
BLOCK1:PERIMETERXZNX2 Fan:ConstantVolume Object has been added
BLOCK1:PERIMETERXZNX1 Fan:ConstantVolume Object has been added
BLOCK1:PERIMETERXZNX3 Fan:ConstantVolume Object has been added
Vent_SP_temp Schedule has been added
AHST_Sch_BLOCK1_PERIMETERXZNX4 Schedule has been added
ACST_Sch_BLOCK1_PERIMETERXZNX4 Schedule has been added
AHST_Sch_BLOCK1_COREXZN Schedule has been added
ACST_Sch_BLOCK1_COREXZN Schedule has been added
AHST_Sch_BLOCK1_PERIMETERXZNX2 Schedule has been added
ACST_Sch_BLOCK1_PERIMETERXZNX2 Schedule has been added
AHST_Sch_BLOCK1_PERIMETERXZNX1 Schedule has been added
ACST_Sch_BLOCK1_PERIMETERXZNX1 Schedule has been added
AHST_Sch_BLOCK1_PERIMETERXZNX3 Schedule has been added
ACST_Sch_BLOCK1_PERIMETERXZNX3 Schedule has been added
Added - SetComfTemp Program
Added - CountHours_BLOCK1_PERIMETERXZNX4 Program
Added - CountHours_BLOCK1_COREXZN Program
Added - CountHours_BLOCK1_PERIMETERXZNX2 Program
Added - CountHours_BLOCK1_PERIMETERXZNX1 Program
Added - CountHours_BLOCK1_PERIMETERXZNX3 Program
Added - SetAppLimits Program
Added - ApplyCAT Program
Added - SetAST Program
Added - SetASTnoTol Program
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX4 Program
Added - SetGeoVarBLOCK1_PERIMETERXZNX4 Program
Added - CountHoursNoApp_BLOCK1_COREXZN Program
Added - SetGeoVarBLOCK1_COREXZN Program
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX2 Program
Added - SetGeoVarBLOCK1_PERIMETERXZNX2 Program
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX1 Program
Added - SetGeoVarBLOCK1_PERIMETERXZNX1 Program
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX3 Program
Added - SetGeoVarBLOCK1_PERIMETERXZNX3 Program
Added - SetInputData Program
Added - SetVOFinputData Program
Added - SetVST Program
Added - ApplyAST_BLOCK1_PERIMETERXZNX4 Program
Added - ApplyAST_BLOCK1_COREXZN Program
Added - ApplyAST_BLOCK1_PERIMETERXZNX2 Program
Added - ApplyAST_BLOCK1_PERIMETERXZNX1 Program
Added - ApplyAST_BLOCK1_PERIMETERXZNX3 Program
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win Program
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win Program
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win Program
Added - Comfort Temperature Output Variable
Added - Adaptive Cooling Setpoint Temperature Output Variable
Added - Adaptive Heating Setpoint Temperature Output Variable
Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable
Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable
Added - z_test_ComfStand Output Variable
Added - z_test_ACSTaul Output Variable
Added - z_test_ACSTall Output Variable
Added - z_test_AHSTaul Output Variable
Added - z_test_AHSTall Output Variable
Added - z_test_CAT Output Variable
Added - z_test_ACSToffset Output Variable
Added - z_test_AHSToffset Output Variable
Added - z_test_ComfMod Output Variable
Added - z_test_ACSTtol Output Variable
Added - z_test_SetpointAcc Output Variable
Added - z_test_CustAST_m Output Variable
Added - z_test_CustAST_n Output Variable
Added - z_test_AHSTtol Output Variable
Added - Ventilation Setpoint Temperature Output Variable
Added - Minimum Outdoor Temperature for ventilation Output Variable
Added - Minimum Outdoor Temperature Difference for ventilation Output Variable
Added - Maximum Outdoor Temperature Difference for ventilation Output Variable
Added - Multiplier for Ventilation Opening Factor Output Variable
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Comfortable Hours_No Applicability_BLOCK1_COREXZN Output Variable
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Comfortable Hours_Applicability_BLOCK1_COREXZN Output Variable
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_COREXZN Output Variable
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_COREXZN Output Variable
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Occupied Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Occupied Hours_BLOCK1_COREXZN Output Variable
Added - Occupied Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Occupied Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Occupied Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Discomfortable Applicable Hot Hours_BLOCK1_COREXZN Output Variable
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Discomfortable Applicable Cold Hours_BLOCK1_COREXZN Output Variable
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_COREXZN Output Variable
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_COREXZN Output Variable
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Zone Floor Area_BLOCK1_COREXZN Output Variable
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Zone Air Volume_BLOCK1_COREXZN Output Variable
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX3 Output Variable
Added - People Occupant Count_BLOCK1_PERIMETERXZNX4 Output Variable
Added - People Occupant Count_BLOCK1_COREXZN Output Variable
Added - People Occupant Count_BLOCK1_PERIMETERXZNX2 Output Variable
Added - People Occupant Count_BLOCK1_PERIMETERXZNX1 Output Variable
Added - People Occupant Count_BLOCK1_PERIMETERXZNX3 Output Variable
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX4 Output Variable
Added - Ventilation Hours_BLOCK1_COREXZN Output Variable
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX2 Output Variable
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX1 Output Variable
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX3 Output Variable
Global variables objects have been added
Internal variables objects have been added
Added - RMOT Sensor
Added - PMOT Sensor
Added - Occ_count_BLOCK1_PERIMETERXZNX4 Sensor
Added - Occ_count_BLOCK1_COREXZN Sensor
Added - Occ_count_BLOCK1_PERIMETERXZNX2 Sensor
Added - Occ_count_BLOCK1_PERIMETERXZNX1 Sensor
Added - Occ_count_BLOCK1_PERIMETERXZNX3 Sensor
Added - BLOCK1_PERIMETERXZNX4_OpT Sensor
Added - BLOCK1_PERIMETERXZNX4_WindSpeed Sensor
Added - BLOCK1_PERIMETERXZNX4_OutT Sensor
Added - BLOCK1_COREXZN_OpT Sensor
Added - BLOCK1_COREXZN_WindSpeed Sensor
Added - BLOCK1_COREXZN_OutT Sensor
Added - BLOCK1_PERIMETERXZNX2_OpT Sensor
Added - BLOCK1_PERIMETERXZNX2_WindSpeed Sensor
Added - BLOCK1_PERIMETERXZNX2_OutT Sensor
Added - BLOCK1_PERIMETERXZNX1_OpT Sensor
Added - BLOCK1_PERIMETERXZNX1_WindSpeed Sensor
Added - BLOCK1_PERIMETERXZNX1_OutT Sensor
Added - BLOCK1_PERIMETERXZNX3_OpT Sensor
Added - BLOCK1_PERIMETERXZNX3_WindSpeed Sensor
Added - BLOCK1_PERIMETERXZNX3_OutT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_OpT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_OutT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_OpT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_OutT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_OpT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_OutT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_OpT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_OutT Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_OpT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_OutT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_OpT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_OutT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_OpT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_OutT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_OpT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_OutT Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_OpT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_OutT Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_Occ_count Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_OpT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_WindSpeed Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_OutT Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_Occ_count Sensor
Added - OutT Sensor
Added - AHST_Act_BLOCK1_PERIMETERXZNX4 Actuator
Added - ACST_Act_BLOCK1:PERIMETERXZNX4 Actuator
Added - AHST_Act_BLOCK1_COREXZN Actuator
Added - ACST_Act_BLOCK1:COREXZN Actuator
Added - AHST_Act_BLOCK1_PERIMETERXZNX2 Actuator
Added - ACST_Act_BLOCK1:PERIMETERXZNX2 Actuator
Added - AHST_Act_BLOCK1_PERIMETERXZNX1 Actuator
Added - ACST_Act_BLOCK1:PERIMETERXZNX1 Actuator
Added - AHST_Act_BLOCK1_PERIMETERXZNX3 Actuator
Added - ACST_Act_BLOCK1:PERIMETERXZNX3 Actuator
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_VentOpenFact Actuator
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_VentOpenFact Actuator
Added - BLOCK1_PERIMETERXZNX4_CoolCoil Sensor
Added - BLOCK1_PERIMETERXZNX4_HeatCoil Sensor
Added - BLOCK1_COREXZN_CoolCoil Sensor
Added - BLOCK1_COREXZN_HeatCoil Sensor
Added - BLOCK1_PERIMETERXZNX2_CoolCoil Sensor
Added - BLOCK1_PERIMETERXZNX2_HeatCoil Sensor
Added - BLOCK1_PERIMETERXZNX1_CoolCoil Sensor
Added - BLOCK1_PERIMETERXZNX1_HeatCoil Sensor
Added - BLOCK1_PERIMETERXZNX3_CoolCoil Sensor
Added - BLOCK1_PERIMETERXZNX3_HeatCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win_HeatCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_CoolCoil Sensor
Added - Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win_HeatCoil Sensor
Added - SetComfTemp Program Calling Manager
Added - CountHours_BLOCK1_PERIMETERXZNX4 Program Calling Manager
Added - CountHours_BLOCK1_COREXZN Program Calling Manager
Added - CountHours_BLOCK1_PERIMETERXZNX2 Program Calling Manager
Added - CountHours_BLOCK1_PERIMETERXZNX1 Program Calling Manager
Added - CountHours_BLOCK1_PERIMETERXZNX3 Program Calling Manager
Added - SetAppLimits Program Calling Manager
Added - ApplyCAT Program Calling Manager
Added - SetAST Program Calling Manager
Added - SetASTnoTol Program Calling Manager
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX4 Program Calling Manager
Added - SetGeoVarBLOCK1_PERIMETERXZNX4 Program Calling Manager
Added - CountHoursNoApp_BLOCK1_COREXZN Program Calling Manager
Added - SetGeoVarBLOCK1_COREXZN Program Calling Manager
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX2 Program Calling Manager
Added - SetGeoVarBLOCK1_PERIMETERXZNX2 Program Calling Manager
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX1 Program Calling Manager
Added - SetGeoVarBLOCK1_PERIMETERXZNX1 Program Calling Manager
Added - CountHoursNoApp_BLOCK1_PERIMETERXZNX3 Program Calling Manager
Added - SetGeoVarBLOCK1_PERIMETERXZNX3 Program Calling Manager
Added - SetInputData Program Calling Manager
Added - SetVOFinputData Program Calling Manager
Added - SetVST Program Calling Manager
Added - ApplyAST_BLOCK1_PERIMETERXZNX4 Program Calling Manager
Added - ApplyAST_BLOCK1_COREXZN Program Calling Manager
Added - ApplyAST_BLOCK1_PERIMETERXZNX2 Program Calling Manager
Added - ApplyAST_BLOCK1_PERIMETERXZNX1 Program Calling Manager
Added - ApplyAST_BLOCK1_PERIMETERXZNX3 Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_0_0_3_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_1_0_2_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_2_0_1_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX4_Wall_4_0_0_3_0_0_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_0_0_3_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_1_0_2_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_2_0_1_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX2_Wall_2_0_0_3_0_0_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_0_0_6_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_1_0_5_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_2_0_4_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_3_0_3_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_4_0_2_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_5_0_1_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX1_Wall_5_0_0_6_0_0_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_0_0_7_Door Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_1_0_6_Door Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_2_0_5_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_3_0_4_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_4_0_3_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_5_0_2_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_6_0_1_Win Program Calling Manager
Added - SetMyVOF_Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win Program Calling Manager
Added - SetWindowOperation_Block1_PerimeterXZNX3_Wall_3_0_0_7_0_0_Win Program Calling Manager
Added - OutputControl:Files object
Added - Output:VariableDictionary object
Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data
Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data
Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data
Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data
Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data
Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data
Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data
Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data
Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data
Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data
Added - z_test_CAT Reporting FrequencyHourly Output:Variable data
Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data
Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data
Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data
Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data
Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data
Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data
Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data
Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data
Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data
Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data
Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data
Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data
Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_No Applicability_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_Applicability_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Comfortable Hours_Applicability_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Occupied Comfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Occupied Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Occupied Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Occupied Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Occupied Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Occupied Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Hot Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Hot Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Cold Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Applicable Cold Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Hot Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Discomfortable Non Applicable Cold Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Zone Floor Area_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Zone Floor Area_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Zone Air Volume_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Zone Air Volume_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - People Occupant Count_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - People Occupant Count_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - People Occupant Count_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - People Occupant Count_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - People Occupant Count_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - Ventilation Hours_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - Ventilation Hours_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data
Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data
Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data
Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data
Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data
Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data
Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data
Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data
Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data
Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data
Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data
Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data
Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data
Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data
Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data
Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data
Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data
Added - AHST_Sch_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - ACST_Sch_BLOCK1_PERIMETERXZNX4 Reporting FrequencyHourly Output:Variable data
Added - AHST_Sch_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - ACST_Sch_BLOCK1_COREXZN Reporting FrequencyHourly Output:Variable data
Added - AHST_Sch_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - ACST_Sch_BLOCK1_PERIMETERXZNX2 Reporting FrequencyHourly Output:Variable data
Added - AHST_Sch_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - ACST_Sch_BLOCK1_PERIMETERXZNX1 Reporting FrequencyHourly Output:Variable data
Added - AHST_Sch_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - ACST_Sch_BLOCK1_PERIMETERXZNX3 Reporting FrequencyHourly Output:Variable data
Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data
Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX4 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX4 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data
Added - BLOCK1_COREXZN VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_COREXZN VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX3 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data
Added - BLOCK1_PERIMETERXZNX3 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data
IDF has been saved
Ending with file:
SmallOffice

=======================END OF GENERIC IDF FILE GENERATION PROCESS=======================

The following IDFs will not work, and therefore these will be deleted:
None

=======================START OF OUTPUT IDF FILES GENERATION PROCESS=======================

The list of output IDFs is going to be:
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
And the total number of output IDFs is going to be 12
Generating the following output IDF files:
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf

=======================END OF OUTPUT IDF FILES GENERATION PROCESS=======================


[7]:

<accim.sim.accis.addAccis at 0x15e23f1f070>

As a result, 12 output IDFs are generated:

[8]:

output_idfs = [i for i in listdir() if i.endswith('.idf') and i not in input_idfs]
print(*output_idfs, sep='\n')

SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf

However, not all of them are necessary. The IDFs that are needed are listed below (also in Table 7 of the research paper):

[9]:

necessary_idfs = [
    'SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf',
    'SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf',
    'SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf',
    'SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf',
    'SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf'
]
print(*necessary_idfs, sep='\n')

SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf

Thus, the remaining, shown below, should be removed to avoid unnecessary computational effort.

[10]:

idfs_to_be_removed = [i for i in listdir() if i.endswith('.idf') and i not in necessary_idfs and i not in input_idfs]
print(*idfs_to_be_removed, sep='\n')
from os import remove
for i in idfs_to_be_removed:
    remove(i)

SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_0[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf

At this point, all EPWs have been formatted to a suitable file name and adaptive setpoint temperatures have been implemented in the input IDFs, so simulations can be run.

3.2. Running simulations

Once EPWs have been formatted and adaptive setpoint temperatures have been implemented in the output IDFs, simulations can be automatically run using the function runEp. Again, the recommended process, suitable for users with no programming background, is:

  • open a CMD terminal pointing at the path where the renamed EPWs and IDFs are located

  • execute Python by entering py or python

  • import the package by entering from accim.run import run

  • call the function by entering run.runEp()

  • enter the required information on CMD terminal (related to the arguments that have not been specified)

In this case study, all arguments are specified:

  • runOnlyAccim is set to True, since only output IDFs need to be simulated;

  • confirmRun is set to True, since the number of simulation runs is known (5 IDFs x 4 EPWs = 20 runs), otherwise, if the user were not sure of the number, it could lead to some memory errors or undesired results;

  • num_CPUs is set to 4, to be suitable for most computers;

  • EnergyPlus_version is set to '23.1', since the EnergyPlus version of the output IDFs is known.

Therefore, no interaction with the user is needed and simulation can be automatically run:

[11]:

from accim.run import run
run.runEp(
    runOnlyAccim=True, #only runs output IDFs, that is, IDFs with "[" in its name
    confirmRun=True, #to skip confirmation
    num_CPUs=4, #to specify the number of CPUs to be used
    EnergyPlus_version='23.1', #to specify the EnergyPlus version of the IDF, and the version of EnergyPlus you are going to run
)

IDD was already set.
The IDFs we are going to run are:
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X.idf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
 and the No. of IDFs is going to be 5
The sample_EPWs we are going to run are:
India_Ahmedabad_Present.epw
India_Ahmedabad_RCP85-2100.epw
India_Shimla_Present.epw
India_Shimla_RCP85-2100.epw
 and the No. of sample_EPWs is going to be 4
Therefore, the simulations are going to be:
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100
 and the No. of simulations is going to be 20

The simulation output files have been saved in the same path. In this case study 20 simulations have been run, therefore there must be 20 CSV files ready to be analysed, which are shown below:

[12]:

from os import listdir
csvs = [i for i in listdir() if i.endswith('.csv') and 'Zsz.csv' not in i and 'Table.csv' not in i and 'Meter.csv' not in i]
print(*csvs, sep='\n')

SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv

3.3. Data analysis

In the following subsections, the CSV files resulting from the simulation runs will be analysed, to provide a table to assess the energy demand differences, and to visualize the data in multiple figures. In this case, it is recommended to use an IDE instead of a CMD terminal.

3.3.1 Tables

The aim of this subsection is to create a table to see how much energy is demanded in the different settings. To do so, the first step would be to generate a DataFrame instance using the class Table, which is going to be stored in variable dataset_runperiod. The arguments are specified as follows:

  • datasets is set to a list of all CSV files except those in which operation mode is NV (HM_1), since energy demand is 0

  • source_frequency is set to 'hourly', since that is the frequency that has been used when the output IDFs were generated.

  • frequency is set to 'runperiod', since the target table is intended to have only 1 row per simulation run

  • frequency_agg_func is set to 'sum', since all hourly energy demand values must be summed on a runperiod frequency basis.

  • standard_outputs is set to True, to use only a controlled range of outputs.

  • level is set only to 'building', since the target table should show the sum of energy demand values from all thermal zones.

  • level_agg_func is set only to 'sum', since only summed values of all zones are necessary.

  • level_excluded_zones is set to 'ATTIC:ATTIC'. If the user opens the IDF file, 2 blocks can be found: ATTIC and Block1. In this case, the block ATTIC contains only one zone named ATTIC as well, which is not air-conditioned. Therefore, it should be omitted in the calculations.

  • split_epw_names is set to True, since the EPWs have previously formatted to analyse the locations and climate scenarios

[13]:

from accim.data.postprocessing.main import Table
dataset_runperiod = Table(
    datasets=[i for i in csvs if 'HM_1' not in i],
    source_frequency='hourly',
    frequency='runperiod',
    frequency_agg_func='sum',
    standard_outputs=True,
    idf_path='SmallOffice.idf',
    level=['building'],
    level_agg_func=['sum'],
    level_excluded_zones=['Attic:Attic'],
    split_epw_names=True,
)

If the user needs to see the DataFrame instance that has been created, it can be accessed at the variable df stored within the class dataset_runperiod as shown below:

[14]:

dataset_runperiod.df

[14]:
Source Model ComfStand CAT ComfMod HVACmode VentCtrl VSToffset MinOToffset MaxWindSpeed ... Building_Total_AFN Zone Infiltration Volume (m3) (summed) Building_Total_AFN Zone Infiltration Air Change Rate (ach) (summed) Building_Total_AFN Zone Ventilation Volume (m3) (summed) Building_Total_AFN Zone Ventilation Air Change Rate (ach) (summed) Building_Total_Cooling Energy Demand (kWh/m2) (summed) Building_Total_Heating Energy Demand (kWh/m2) (summed) Building_Total_Cooling Energy Consumption (kWh/m2) (summed) Building_Total_Heating Energy Consumption (kWh/m2) (summed) Building_Total_Total Energy Demand (kWh/m2) (summed) Building_Total_Total Energy Consumption (kWh/m2) (summed)
0 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... 347243.806542 1322.235976 5.320764e+05 1800.058242 422.637219 0.000000 243.654066 0.000000 422.637219 243.654066
1 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... 205396.684898 774.301075 4.312486e+05 1463.410388 488.483076 0.000000 299.886114 0.000000 488.483076 299.886114
2 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... 625439.687662 2424.670514 6.310661e+05 2132.734801 243.026030 0.516401 126.151453 0.429475 243.542431 126.580928
3 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... 481241.805737 1865.864529 5.207283e+05 1764.116608 314.229336 0.008982 169.445054 0.007290 314.238318 169.452344
4 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 505436.820838 1936.192351 6.548314e+05 2210.483589 323.638223 0.000000 201.904814 0.000000 323.638223 201.904814
5 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 407551.586559 1555.919639 5.859295e+05 1980.983955 365.841209 0.000000 242.135624 0.000000 365.841209 242.135624
6 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 596204.449816 2311.065075 6.061140e+05 2049.153259 273.697114 0.000000 150.241174 0.000000 273.697114 150.241174
7 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 523549.542700 2029.502943 5.544448e+05 1876.135334 302.830389 0.000000 173.563777 0.000000 302.830389 173.563777
8 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 ... 253320.799281 968.313650 5.711213e+07 211511.574369 149.854515 2.721968 110.929240 1.217036 152.576482 112.146276
9 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 ... 265506.895621 1010.120488 3.656631e+07 135567.542056 251.308366 0.275772 182.059818 0.119917 251.584138 182.179736
10 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 ... 292049.767442 1132.591254 6.349492e+07 237033.450170 67.832882 21.452031 44.609426 9.603888 89.284913 54.213314
11 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 ... 235576.989638 914.614985 6.630709e+07 247468.585892 96.686088 8.483847 67.215108 3.787520 105.169935 71.002628
12 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 429422.000743 1640.776468 5.949321e+05 2010.671739 380.164770 0.000000 230.241728 0.000000 380.164770 230.241728
13 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 307681.555517 1168.838236 5.081313e+05 1720.641207 430.326584 0.000000 276.730871 0.000000 430.326584 276.730871
14 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 602070.452334 2334.008055 6.114396e+05 2066.768803 266.360903 0.000000 143.694495 0.000000 266.360903 143.694495
15 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... 495824.382077 1922.104088 5.319426e+05 1801.328256 316.727614 0.000000 178.015116 0.000000 316.727614 178.015116

16 rows × 182 columns

Then, the method format_table needs to be executed, to filter only energy demand columns at building level, where:

  • type_of_table is set to 'custom', so that specifically the columns specified in custom_cols are filtered

  • custom_cols is a list of the columns that should be kept, namely heating, cooling and total energy demand at building level

To know exactly the name of the energy demand columns at building level, the user can access to the DataFrame instance and search in the columns. For instance, the code below searches the string “energy demand” in all columns and print the results.

[15]:

energy_demand_cols = [i for i in dataset_runperiod.df.columns if 'energy demand' in i.lower()]
print(*energy_demand_cols, sep='\n')

Block1:PerimeterXZNX4_Cooling Energy Demand (kWh/m2)
Block1:CoreXZN_Cooling Energy Demand (kWh/m2)
Block1:PerimeterXZNX2_Cooling Energy Demand (kWh/m2)
Block1:PerimeterXZNX1_Cooling Energy Demand (kWh/m2)
Block1:PerimeterXZNX3_Cooling Energy Demand (kWh/m2)
Block1:PerimeterXZNX4_Heating Energy Demand (kWh/m2)
Block1:CoreXZN_Heating Energy Demand (kWh/m2)
Block1:PerimeterXZNX2_Heating Energy Demand (kWh/m2)
Block1:PerimeterXZNX1_Heating Energy Demand (kWh/m2)
Block1:PerimeterXZNX3_Heating Energy Demand (kWh/m2)
Block1:PerimeterXZNX4_Total Energy Demand (kWh/m2) (summed)
Block1:CoreXZN_Total Energy Demand (kWh/m2) (summed)
Block1:PerimeterXZNX2_Total Energy Demand (kWh/m2) (summed)
Block1:PerimeterXZNX1_Total Energy Demand (kWh/m2) (summed)
Block1:PerimeterXZNX3_Total Energy Demand (kWh/m2) (summed)
Building_Total_Cooling Energy Demand (kWh/m2) (summed)
Building_Total_Heating Energy Demand (kWh/m2) (summed)
Building_Total_Total Energy Demand (kWh/m2) (summed)

In this case, only columns at building level are necessary, therefore these are specified when the format_table method is called below:

[16]:

dataset_runperiod.format_table(
    type_of_table='custom',
    custom_cols=[
        'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
        'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
        'Building_Total_Total Energy Demand (kWh/m2) (summed)'
    ]
)

Again, if the user needs to see how the DataFrame instance has been modified, it can be accessed at the variable df stored within the class dataset_runperiod as shown below:

[17]:

dataset_runperiod.df

[17]:
Model ComfStand CAT ComfMod HVACmode VentCtrl VSToffset MinOToffset MaxWindSpeed ASTtol ... EPW Source EPW_Country_name EPW_City_or_subcountry EPW_Scenario-Year EPW_Scenario EPW_Year Building_Total_Cooling Energy Demand (kWh/m2) (summed) Building_Total_Heating Energy Demand (kWh/m2) (summed) Building_Total_Total Energy Demand (kWh/m2) (summed)
0 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... India Ahmedabad Present Present Present 422.637219 0.000000 422.637219
1 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... India Ahmedabad RCP85-2100 RCP85 2100 488.483076 0.000000 488.483076
2 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... India Shimla Present Present Present 243.026030 0.516401 243.542431
3 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[V... India Shimla RCP85-2100 RCP85 2100 314.229336 0.008982 314.238318
4 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... India Ahmedabad Present Present Present 323.638223 0.000000 323.638223
5 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... India Ahmedabad RCP85-2100 RCP85 2100 365.841209 0.000000 365.841209
6 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... India Shimla Present Present Present 273.697114 0.000000 273.697114
7 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[V... India Shimla RCP85-2100 RCP85 2100 302.830389 0.000000 302.830389
8 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 AT_0.1 ... India_Ahmedabad_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... India Ahmedabad Present Present Present 149.854515 2.721968 152.576482
9 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 AT_0.1 ... India_Ahmedabad_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... India Ahmedabad RCP85-2100 RCP85 2100 251.308366 0.275772 251.584138
10 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 AT_0.1 ... India_Shimla_Present SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... India Shimla Present Present Present 67.832882 21.452031 89.284913
11 SmallOffice CS_IND IMAC C NV CA_80 CM_3 HM_2 VC_0 VO_0.0 MT_50.0 MW_50.0 AT_0.1 ... India_Shimla_RCP85-2100 SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V... India Shimla RCP85-2100 RCP85 2100 96.686088 8.483847 105.169935
12 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_Present SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... India Ahmedabad Present Present Present 380.164770 0.000000 380.164770
13 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Ahmedabad_RCP85-2100 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... India Ahmedabad RCP85-2100 RCP85 2100 430.326584 0.000000 430.326584
14 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_Present SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... India Shimla Present Present Present 266.360903 0.000000 266.360903
15 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X AT_0.1 ... India_Shimla_RCP85-2100 SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC... India Shimla RCP85-2100 RCP85 2100 316.727614 0.000000 316.727614

16 rows × 21 columns

At this point, the user might have a vague idea of what table can be done with the available data, especially if there is a large number of categorical variables. In order to get a clearer idea of this, the user can call the method named gather_vars_query. It only takes one argument, named vars_to_gather, which should be a list of the variables to be combined.

In this case, when IDFs with adaptive setpoint were generated, the arguments where more than one than one value were requested were ComfStand, ComfMod and HVACmode (ComfStand=[2, 7], ComfMod=[0, 3], HVACmode=[0, 1, 2]). Therefore, these are all the categorical variables that change regarding the IDFs and the possibilities that might be interesting to study, and therefore the variables that have been entered in vars_to_gather.

Then, after calling gather_vars_query, accim prints on screen the categorical variables that contains more than one different value (i.e. ComfMod, since values are “CM_0” and “CM_3”), and the different combinations based on the combined variables, joined by character “[“:

[18]:

dataset_runperiod.gather_vars_query(
    vars_to_gather=[
        'ComfStand',
        'ComfMod',
        'HVACmode'
    ]
)

The categorical columns which have different values and those values are:
ComfStand: ['CS_IND IMAC C NV', 'CS_INT ASHRAE55']
ComfMod: ['CM_0', 'CM_3']
HVACmode: ['HM_0', 'HM_2']
EPW_City_or_subcountry: ['Ahmedabad', 'Shimla']
EPW_Scenario-Year: ['Present', 'RCP85-2100']
EPW_Scenario: ['Present', 'RCP85']
EPW_Year: ['Present', '2100']
The available options resulting from the data entered in vars_to_gather would be:
CS_IND IMAC C NV[CM_0[HM_0
CS_IND IMAC C NV[CM_3[HM_0
CS_IND IMAC C NV[CM_3[HM_2
CS_INT ASHRAE55[CM_3[HM_0

At this point, the user should be aware of the possibilities to be studied, and therefore can confidently run the method wrangled_table to get the target table, where the following arguments are specified:

  • reshaping is set to 'unstack', since some variables in the rows should be moved to the columns.

  • vars_to_gather is set to “ComfStand”, “ComfMod” and “HVACmode”, as previously showed in method gather_vars_query.

  • baseline is set to 'CS_IND IMAC C NV[CM_3[HM_2', which is the setting that this case study intends to analyse.

  • vars_to_keep is set to 'EPW_City_or_subcountry', 'EPW_Scenario' and 'EPW_Year', since these are the remaining categorical variables apart from those specified in vars_to_gather.

  • comparison_mode is only set to 'baseline compared to others', since only the variance of the baseline respect to all other combinations must be shown.

  • comparison_cols is only set to 'relative', since absolute differences are not needed.

  • rename_dict is a dictionary following the acronyms from Table 7 in research paper and available options previously shown in method gather_vars_query.

  • transpose is set to True, since the untransposed was not in a suitable shape.

[19]:

dataset_runperiod.wrangled_table(
    reshaping='unstack',
    vars_to_gather=['ComfStand', 'ComfMod', 'HVACmode'],
    baseline='CS_IND IMAC C NV[CM_3[HM_2',
    vars_to_keep=['EPW_City_or_subcountry', 'EPW_Scenario', 'EPW_Year'],
    comparison_mode=['baseline compared to others'],
    comparison_cols=['relative'],
    rename_dict={
        'CS_IND IMAC C NV[CM_0[HM_0': 'Ind_Stat_AC',
        'CS_IND IMAC C NV[CM_3[HM_0': 'Ind_Adap_AC',
        'CS_IND IMAC C NV[CM_3[HM_2': 'Ind_Adap_MM',
        'CS_INT ASHRAE55[CM_3[HM_0': 'ASH_Adap_AC'
    },
    transpose=True,
    excel_filename='testing_accim'
)

Finally, an Excel spreadsheet named 'testing_accim.xlsx' as specified in excel_filename argument, is created and saved in the same path where the method was called. The Excel spreadsheet is read and shown below (also in Table 8, in the research paper):

[20]:

import pandas as pd
output_xlsx = pd.read_excel('testing_accim.xlsx', index_col=[0, 1], header=[0, 1, 2])
output_xlsx

[20]:
EPW_City_or_subcountry Ahmedabad Shimla
EPW_Scenario Present RCP85 Present RCP85
EPW_Year Present 2100 Present 2100
Building_Total_Cooling Energy Demand (kWh/m2) (summed) Ind_Stat_AC 422.64 488.48 243.03 314.23
Ind_Adap_AC 323.64 365.84 273.70 302.83
Ind_Adap_MM 149.85 251.31 67.83 96.69
ASH_Adap_AC 380.16 430.33 266.36 316.73
1-(Ind_Adap_MM/Ind_Stat_AC) 0.65 0.49 0.72 0.69
1-(Ind_Adap_MM/Ind_Adap_AC) 0.54 0.31 0.75 0.68
1-(Ind_Adap_MM/ASH_Adap_AC) 0.61 0.42 0.75 0.69
Building_Total_Heating Energy Demand (kWh/m2) (summed) Ind_Stat_AC 0.00 0.00 0.52 0.01
Ind_Adap_AC 0.00 0.00 0.00 0.00
Ind_Adap_MM 2.72 0.28 21.45 8.48
ASH_Adap_AC 0.00 0.00 0.00 0.00
1-(Ind_Adap_MM/Ind_Stat_AC) -inf -inf -40.54 -943.54
1-(Ind_Adap_MM/Ind_Adap_AC) -inf -inf -inf -inf
1-(Ind_Adap_MM/ASH_Adap_AC) -inf -inf -inf -inf
Building_Total_Total Energy Demand (kWh/m2) (summed) Ind_Stat_AC 422.64 488.48 243.54 314.24
Ind_Adap_AC 323.64 365.84 273.70 302.83
Ind_Adap_MM 152.58 251.58 89.28 105.17
ASH_Adap_AC 380.16 430.33 266.36 316.73
1-(Ind_Adap_MM/Ind_Stat_AC) 0.64 0.48 0.63 0.67
1-(Ind_Adap_MM/Ind_Adap_AC) 0.53 0.31 0.67 0.65
1-(Ind_Adap_MM/ASH_Adap_AC) 0.60 0.42 0.66 0.67

The user can also access the wrangled_table output at the variable stored within the class dataset_runperiod, depending on the reshaping method used:

  • for 'pivot', 'wrangled_df_pivoted'

  • for 'unstack', 'wrangled_df_unstacked',

  • for 'stack', 'wrangled_df_stacked',

  • for 'multiindex', 'wrangled_df_multiindex'

In this case, with 'unstack':

[21]:

dataset_runperiod.wrangled_df_unstacked

[21]:
EPW_City_or_subcountry Ahmedabad Shimla
EPW_Scenario Present RCP85 Present RCP85
EPW_Year Present 2100 Present 2100
Building_Total_Cooling Energy Demand (kWh/m2) (summed) Ind_Stat_AC 422.64 488.48 243.03 314.23
Ind_Adap_AC 323.64 365.84 273.70 302.83
Ind_Adap_MM 149.85 251.31 67.83 96.69
ASH_Adap_AC 380.16 430.33 266.36 316.73
1-(Ind_Adap_MM/Ind_Stat_AC) 0.65 0.49 0.72 0.69
1-(Ind_Adap_MM/Ind_Adap_AC) 0.54 0.31 0.75 0.68
1-(Ind_Adap_MM/ASH_Adap_AC) 0.61 0.42 0.75 0.69
Building_Total_Heating Energy Demand (kWh/m2) (summed) Ind_Stat_AC 0.00 0.00 0.52 0.01
Ind_Adap_AC 0.00 0.00 0.00 0.00
Ind_Adap_MM 2.72 0.28 21.45 8.48
ASH_Adap_AC 0.00 0.00 0.00 0.00
1-(Ind_Adap_MM/Ind_Stat_AC) -inf -inf -40.54 -943.54
1-(Ind_Adap_MM/Ind_Adap_AC) -inf -inf -inf -inf
1-(Ind_Adap_MM/ASH_Adap_AC) -inf -inf -inf -inf
Building_Total_Total Energy Demand (kWh/m2) (summed) Ind_Stat_AC 422.64 488.48 243.54 314.24
Ind_Adap_AC 323.64 365.84 273.70 302.83
Ind_Adap_MM 152.58 251.58 89.28 105.17
ASH_Adap_AC 380.16 430.33 266.36 316.73
1-(Ind_Adap_MM/Ind_Stat_AC) 0.64 0.48 0.63 0.67
1-(Ind_Adap_MM/Ind_Adap_AC) 0.53 0.31 0.67 0.65
1-(Ind_Adap_MM/ASH_Adap_AC) 0.60 0.42 0.66 0.67

3.3.2. Data visualization

The aim of this subsection is to plot figures that represent the results of the building energy simulations with adaptive setpoint temperatures. Methods scatter_plot, scatter_plot_with_baseline and time_plot need to be called after method format_table. However, the class Table instance created at the previous section had runperiod frequency, which would be translated into very simple and not interesting figures, and besides NV mode was not considered. Therefore, a new instance will be created in this case using hourly frequency, in order to plot hourly values of indoor temperature and energy demand among others.

[22]:

from accim.data.postprocessing.main import Table
dataset_hourly = Table(
    source_frequency='hourly',
    frequency='hourly',
    frequency_agg_func='sum',
    standard_outputs=True,
    idf_path='SmallOffice.idf',
    level=['building'],
    level_agg_func=['mean', 'sum'],
    level_excluded_zones=['Attic:Attic'],
    split_epw_names=True,
)

Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_1[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[VC_0[VO_0.0[MT_50.0[MW_50.0[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv is hourly, therefore no aggregation will be performed.
Input data frequency in file SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.csv is hourly, therefore no aggregation will be performed.
Please bear in mind if you are using CSVs with multiple frequencies, you will get NaNs. The following rows have NaN values:
            Date/Time  \
3803  06/08  12:00:00
3877  06/11  14:00:00

      Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)  \
3803                                          35.458333
3877                                          41.833333

      Environment:Site Outdoor Air Relative Humidity [%](Hourly)  \
3803                                          48.666667
3877                                          39.833333

      Environment:Site Wind Speed [m/s](Hourly)  \
3803                                   3.166667
3877                                   3.400000

      EMS:Comfort Temperature [C](Hourly)  \
3803                            33.494712
3877                            29.565440

      EMS:Adaptive Cooling Setpoint Temperature [C](Hourly)  \
3803                                              33.56
3877                                              31.58

      EMS:Adaptive Heating Setpoint Temperature [C](Hourly)  \
3803                                              25.57
3877                                              24.79

      EMS:Adaptive Cooling Setpoint Temperature_No Tolerance [C](Hourly)  \
3803                                             33.670
3877                                             31.685

      EMS:Adaptive Heating Setpoint Temperature_No Tolerance [C](Hourly)  \
3803                                             25.470
3877                                             24.685

      EMS:Ventilation Setpoint Temperature [C](Hourly)  ...  \
3803                                            29.565  ...
3877                                            28.185  ...

      VRF OUTDOOR UNIT_BLOCK1:PERIMETERXZNX3:VRF Heat Pump Cooling Electricity Energy [J](Hourly)  \
3803                                       5.753468e+06
3877                                       1.155579e+07

      VRF OUTDOOR UNIT_BLOCK1:PERIMETERXZNX3:VRF Heat Pump Heating Electricity Energy [J](Hourly)  \
3803                                                0.0
3877                                                0.0

                                                 Source  Month/Day  Hour  \
3803  SmallOffice[CS_IND IMAC C NV[CA_80[CM_3[HM_2[V...             None
3877  SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC...             None

      Month   Day  Minute  Second  count
3803         None    None    None      1
3877         None    None    None      1

[2 rows x 135 columns]

In this case, format_table will be called using the parameter type_of_table “temperature”, which will provide the mean operative temperature of all air conditioned zones, the adaptive setpoint temperatures, a value of running mean outdoor temperature of a single zone (since it is the same for all of them), and the building total heating and cooling energy demands.

[23]:

dataset_hourly.format_table(
    type_of_table='custom',
    custom_cols=[
        'Block1:PerimeterXZNX4_ASHRAE 55 Running mean outdoor temperature (°C)',
        'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
        'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
        'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
        'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
        'Building_Total_Zone Operative Temperature (°C) (mean)'
    ]
)

Now, the user can see which are the columns of the DataFrame instance:

[24]:

dataset_hourly.df.columns

[24]:

Index(['Date/time', 'Model', 'ComfStand', 'CAT', 'ComfMod', 'HVACmode',
       'VentCtrl', 'VSToffset', 'MinOToffset', 'MaxWindSpeed', 'ASTtol',
       'NameSuffix', 'EPW', 'Source', 'Month', 'Day', 'Hour',
       'EPW_Country_name', 'EPW_City_or_subcountry', 'EPW_Scenario-Year',
       'EPW_Scenario', 'EPW_Year',
       'Block1:PerimeterXZNX4_ASHRAE 55 Running mean outdoor temperature (°C)',
       'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
       'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
       'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
       'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
       'Building_Total_Zone Operative Temperature (°C) (mean)'],
      dtype='object')

And the DataFrame itself:

[25]:

dataset_hourly.df

[25]:
Date/time Model ComfStand CAT ComfMod HVACmode VentCtrl VSToffset MinOToffset MaxWindSpeed ... EPW_City_or_subcountry EPW_Scenario-Year EPW_Scenario EPW_Year Block1:PerimeterXZNX4_ASHRAE 55 Running mean outdoor temperature (°C) Building_Total_Cooling Energy Demand (kWh/m2) (summed) Building_Total_Heating Energy Demand (kWh/m2) (summed) Adaptive Cooling Setpoint Temperature_No Tolerance (°C) Adaptive Heating Setpoint Temperature_No Tolerance (°C) Building_Total_Zone Operative Temperature (°C) (mean)
0 01/01 01:00:00 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... Ahmedabad Present Present Present 20.659722 0.002979 0.0 27.312882 21.872882 26.232696
1 01/01 02:00:00 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... Ahmedabad Present Present Present 20.659722 0.000000 0.0 27.581458 22.141458 26.630590
2 01/01 03:00:00 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... Ahmedabad Present Present Present 20.659722 0.000000 0.0 27.581458 22.141458 26.729746
3 01/01 04:00:00 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... Ahmedabad Present Present Present 20.659722 0.000000 0.0 27.581458 22.141458 26.759263
4 01/01 05:00:00 SmallOffice CS_IND IMAC C NV CA_80 CM_0 HM_0 VC_X VO_X MT_X MW_X ... Ahmedabad Present Present Present 20.659722 0.000000 0.0 27.581458 22.141458 26.755771
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
175195 12/31 20:00:00 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... Shimla RCP85-2100 RCP85 2100 11.446157 0.032432 0.0 24.848309 17.848309 24.725657
175196 12/31 21:00:00 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... Shimla RCP85-2100 RCP85 2100 11.446157 0.005216 0.0 24.848309 17.848309 24.659706
175197 12/31 22:00:00 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... Shimla RCP85-2100 RCP85 2100 11.446157 0.003844 0.0 24.848309 17.848309 24.496601
175198 12/31 23:00:00 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... Shimla RCP85-2100 RCP85 2100 11.446157 0.003540 0.0 24.848309 17.848309 24.309527
175199 12/31 24:00:00 SmallOffice CS_INT ASHRAE55 CA_80 CM_3 HM_0 VC_X VO_X MT_X MW_X ... Shimla RCP85-2100 RCP85 2100 11.446157 0.003302 0.0 24.848309 17.848309 24.120236

175200 rows × 28 columns

Once format_table has been called, the methods to create the figures can follow. First, scatter_plot method will be used, in which the typical plot to show the linear regression of adaptive comfort models will be generated. As specified in the arguments below, the variables “ComfStand”, “ComfMod” and “HVACmode” were gathered and the different combinations are plotted in rows, while “EPW_City_or_subcountry” and “EPW_Scenario-Year” combinations are plotted in the columns. Data on main y-axis (left) was heating and cooling energy demand, while setpoint temperatures and operative temperature were plotted on the secondary y-axis (right), following the colours specified in the arguments. Finally, these were renamed following the dictionaries entered.

[27]:

dataset_hourly.scatter_plot(
    vars_to_gather_rows=['ComfStand', 'ComfMod', 'HVACmode'],
    vars_to_gather_cols=['EPW_City_or_subcountry', 'EPW_Scenario-Year'],
    data_on_x_axis='Block1:PerimeterXZNX4_ASHRAE 55 Running mean outdoor temperature (°C)',
    data_on_y_main_axis=[
        [
            'Energy Demand (kWh/m2)',
            [
                'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
                'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
            ]
        ],
    ],
    data_on_y_sec_axis=[
        [
            'Temperature (°C)',
            [
                'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
                'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
                'Building_Total_Zone Operative Temperature (°C) (mean)'
            ]
        ],
    ],
    colorlist_y_main_axis=[
        [
            'Energy Demand (kWh/m2)',
            [
                'cyan',
                'orange',
            ]
        ],
    ],
    colorlist_y_sec_axis=[
        [
            'Temperature (°C)',
            [
                'blue',
                'red',
                'green'
            ]
        ],
    ],
    #best_fit_deg_y_main_axis=[
        #[
            #'Energy Demand (kWh/m2)',
            #[
                #1,
                #1,
            #]
        #],
    #],
    cols_renaming_dict={
        'Ahmedabad[Present': 'Ahmedabad Present',
        'Ahmedabad[RCP85-2100': 'Ahmedabad RCP85-2100',
        'Shimla[Present': 'Shimla Present',
        'Shimla[RCP85-2100': 'Shimla RCP85-2100'
    },
    rows_renaming_dict={
        'CS_IND IMAC C NV[CM_0[HM_0': 'IND_Stat_AC',
        'CS_IND IMAC C NV[CM_3[HM_0': 'IND_Adap_AC',
        'CS_IND IMAC C NV[CM_3[HM_1': 'IND_Adap_NV',
        'CS_IND IMAC C NV[CM_3[HM_2': 'IND_Adap_MM',
        'CS_INT ASHRAE55[CM_3[HM_0': 'ASH_Adap_AC'
    },
    sharex=False,
    sharey=False,
    supxlabel='Prevailing mean outdoor temperature (°C)',
    figname='testing_scatterplot_case_study',
    figsize=4,
    ratio_height_to_width=0.5,
    dpi=300,
    confirm_graph=True
)

The number of rows and the list of these is going to be:
No. of rows = 5
List of rows:
CS_IND IMAC C NV[CM_0[HM_0
CS_IND IMAC C NV[CM_3[HM_0
CS_IND IMAC C NV[CM_3[HM_1
CS_IND IMAC C NV[CM_3[HM_2
CS_INT ASHRAE55[CM_3[HM_0
The renamed rows are going to be:
IND_Stat_AC
IND_Adap_AC
IND_Adap_NV
IND_Adap_MM
ASH_Adap_AC
The number of columns and the list of these is going to be:
No. of columns = 4
List of columns:
Ahmedabad[Present
Ahmedabad[RCP85-2100
Shimla[Present
Shimla[RCP85-2100
The renamed columns are going to be:
Ahmedabad Present
Ahmedabad RCP85-2100
Shimla Present
Shimla RCP85-2100
../../_images/jupyter_notebooks_research_paper_case_study_research_paper_case_study_67_1.png

Using a similar code snippet but changing method to time_plot and omitting the argument data_on_x_axis, a figure with time on the x-axis can be generated. In this case, the argument sharex is also omitted, since the simulation period is the same for all cases.

[28]:

dataset_hourly.time_plot(
    vars_to_gather_rows=['ComfStand', 'ComfMod', 'HVACmode'],
    vars_to_gather_cols=['EPW_City_or_subcountry', 'EPW_Scenario-Year'],
    data_on_y_main_axis=[
        [
            'Energy Demand (kWh/m2)',
            [
                'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
                'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
            ]
        ],
    ],
    data_on_y_sec_axis=[
        [
            'Temperature (°C)',
            [
                'Adaptive Cooling Setpoint Temperature_No Tolerance (°C)',
                'Adaptive Heating Setpoint Temperature_No Tolerance (°C)',
                'Building_Total_Zone Operative Temperature (°C) (mean)'
            ]
        ],
    ],
    colorlist_y_main_axis=[
        [
            'Energy Demand (kWh/m2)',
            [
                'cyan',
                'orange',
            ]
        ],
    ],
    colorlist_y_sec_axis=[
        [
            'Temperature (°C)',
            [
                'blue',
                'red',
                'green'
            ]
        ],
    ],
    cols_renaming_dict={
        'Ahmedabad[Present': 'Ahmedabad Present',
        'Ahmedabad[RCP85-2100': 'Ahmedabad RCP85-2100',
        'Shimla[Present': 'Shimla Present',
        'Shimla[RCP85-2100': 'Shimla RCP85-2100'
    },
    rows_renaming_dict={
        'CS_IND IMAC C NV[CM_0[HM_0': 'IND_Stat_AC',
        'CS_IND IMAC C NV[CM_3[HM_0': 'IND_Adap_AC',
        'CS_IND IMAC C NV[CM_3[HM_1': 'IND_Adap_NV',
        'CS_IND IMAC C NV[CM_3[HM_2': 'IND_Adap_MM',
        'CS_INT ASHRAE55[CM_3[HM_0': 'ASH_Adap_AC'
    },
    sharey=True,
    figname='testing_timeplot_case_study',
    figsize=4,
    ratio_height_to_width=0.5,
    dpi=300,
    confirm_graph=True
)

The number of rows and the list of these is going to be:
No. of rows = 5
List of rows:
CS_IND IMAC C NV[CM_0[HM_0
CS_IND IMAC C NV[CM_3[HM_0
CS_IND IMAC C NV[CM_3[HM_1
CS_IND IMAC C NV[CM_3[HM_2
CS_INT ASHRAE55[CM_3[HM_0
The renamed rows are going to be:
IND_Stat_AC
IND_Adap_AC
IND_Adap_NV
IND_Adap_MM
ASH_Adap_AC
The number of columns and the list of these is going to be:
No. of columns = 4
List of columns:
Ahmedabad[Present
Ahmedabad[RCP85-2100
Shimla[Present
Shimla[RCP85-2100
The renamed columns are going to be:
Ahmedabad Present
Ahmedabad RCP85-2100
Shimla Present
Shimla RCP85-2100

C:\Users\usuario\AppData\Local\Programs\Python\Python39\lib\site-packages\accim\data\postprocessing\main.py:3740: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  start_date = datetime.datetime.strptime(self.df_for_graph['Date/time'][0], freq_graph_dict[self.frequency][1])
C:\Users\usuario\AppData\Local\Programs\Python\Python39\lib\site-packages\accim\data\postprocessing\main.py:3744: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
  self.df_for_graph['Date/time'] = pd.date_range(
../../_images/jupyter_notebooks_research_paper_case_study_research_paper_case_study_69_2.png

Finally, using scatter_plot_with_baseline a different scatter plot can be generated. In this case, it is mainly used to compare some combination of variables gathered in columns with the remaining variants. In the example below, the total heating and cooling energy demands will be compared using the combination 'CS_IND IMAC C NV[CM_3[HM_2' (or IND_Adap_MM) as the baseline, since it is expected to provide the greater energy savings. The same Table instance will be used, however, to avoid plotting an empty figure, since energy demand is null in the combination 'CS_IND IMAC C NV[CM_3[HM_1' (or IND_Adap_NV), since it is on naturally ventilated mode, the argument detailed_cols is used. In this argument, all the combinations to be plotted are specified, that is, all of them except the NV and the baseline.

[29]:

dataset_hourly.scatter_plot_with_baseline(
    vars_to_gather_rows=['EPW_City_or_subcountry', 'EPW_Scenario-Year'],
    vars_to_gather_cols=['ComfStand', 'ComfMod', 'HVACmode'],
    detailed_cols=[
        'CS_IND IMAC C NV[CM_0[HM_0',
        'CS_IND IMAC C NV[CM_3[HM_0',
        'CS_INT ASHRAE55[CM_3[HM_0'
    ],
    data_on_y_axis_baseline_plot=[
                'Building_Total_Cooling Energy Demand (kWh/m2) (summed)',
                'Building_Total_Heating Energy Demand (kWh/m2) (summed)',
    ],
    colorlist_baseline_plot_data=[
                'blue',
                'red'
    ],
    baseline='CS_IND IMAC C NV[CM_3[HM_2',
    rows_renaming_dict={
        'Ahmedabad[Present': 'Ahmedabad Present',
        'Ahmedabad[RCP85-2100': 'Ahmedabad RCP85-2100',
        'Shimla[Present': 'Shimla Present',
        'Shimla[RCP85-2100': 'Shimla RCP85-2100'
    },
    cols_renaming_dict={
        'CS_IND IMAC C NV[CM_0[HM_0': 'IND_Stat_AC',
        'CS_IND IMAC C NV[CM_3[HM_0': 'IND_Adap_AC',
        'CS_INT ASHRAE55[CM_3[HM_0': 'ASH_Adap_AC'
    },
    supxlabel='IND_Adap_MM Hourly Energy Demand (kWh/m²)',
    supylabel='Reference Hourly Energy Demand (kWh/m²)',
    figname='testing_scatterplotbaseline_case_study',
    figsize=3,
    dpi=300,
    confirm_graph=True
)

The number of rows and the list of these is going to be:
No. of rows = 4
List of rows:
Ahmedabad[Present
Ahmedabad[RCP85-2100
Shimla[Present
Shimla[RCP85-2100
The renamed rows are going to be:
Ahmedabad Present
Ahmedabad RCP85-2100
Shimla Present
Shimla RCP85-2100
The number of columns and the list of these is going to be:
No. of columns = 3
List of columns:
CS_IND IMAC C NV[CM_0[HM_0
CS_IND IMAC C NV[CM_3[HM_0
CS_INT ASHRAE55[CM_3[HM_0
The renamed columns are going to be:
IND_Stat_AC
IND_Adap_AC
ASH_Adap_AC
../../_images/jupyter_notebooks_research_paper_case_study_research_paper_case_study_71_1.png

Finally, so that this jupyter notebook can be properly run again, all files different than the input ones will be deleted.

[30]:

for i in input_epws:
    shutil.move(f'backup/{i}', i)

[31]:

files_to_delete = [i for i in listdir() if i not in input_files]
print(*files_to_delete, sep='\n')

India_Ahmedabad_Present.epw
India_Ahmedabad_RCP85-2100.epw
India_Shimla_Present.epw
India_Shimla_RCP85-2100.epw
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X.idf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.audit
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.bnd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.dxf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.eio
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.end
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.err
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.eso
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.mdd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.mtd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.rdd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.rvaudit
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_Present.shd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_PresentTable.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_PresentTable.htm
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_PresentZsz.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.audit
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.bnd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.dxf
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.eio
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.end
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.err
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.eso
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.mdd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.mtd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.rdd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.rvaudit
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.shd
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100Table.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100Table.htm
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100Zsz.csv
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.audit
SmallOffice[CS_IND IMAC C NV[CA_80[CM_0[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.bnd
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.eio
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.err
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.mtd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.rdd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.rvaudit
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100.shd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Ahmedabad_RCP85-2100Table.csv
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.bnd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.dxf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.eio
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.end
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_Present.err
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_PresentTable.htm
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_PresentZsz.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.audit
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.bnd
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.dxf
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.eio
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.end
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.err
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.eso
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SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.rdd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.rvaudit
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100.shd
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100Table.csv
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100Table.htm
SmallOffice[CS_INT ASHRAE55[CA_80[CM_3[HM_0[VC_X[VO_X[MT_X[MW_X[AT_0.1[NS_X[India_Shimla_RCP85-2100Zsz.csv
testing_accim.xlsx
testing_scatterplotbaseline_case_study.png
testing_scatterplot_case_study.png
testing_timeplot_case_study.png

[32]:

for i in files_to_delete:
    remove(i)