Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization
This repository contains the research compendium for our paper:
Martín Mosteiro-Romero, Clayton Miller, Adrian Chong, and Rudi Stouffs (2022). Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization. Submitted to Building and Environment.
The compendium includes all the data and code needed to reproduce the analysis and figures associated with the publication.
This GitHub repository comprises the following files and folders:
full_paper_workflow.ipynb: Jupyter notebook with the code and documentation to replicate this studymeter_data- Hourly cooling demand by building for the period 2018-2020
- Hourly electricity demand by building for the period 2018-2020
- Hourly number of devices connected to the WiFi network by building for the period 2018-2020
- Weather station data for the years 2018 and 2020
CEA_model: City Energy Analyst (CEA) modelsbaseline: Inputs to the CEA model of the university campus using archetypal CEA inputs for the building propertiescalibration: Inputs to the calibrated CEA model of the university campus for the years 2018 and 2020, as well as the previously-run calibration assessment metrics for two runs of the calibration procedureregression: Inputs to the calibrated CEA model of the university campus after using the regression model for electricity for the years 2018 and 2020, as well as the previously-run regression model assessment metricsbaseline_calibration_regression_comparison: Summary of the comparison metrics for each of the versions of the CEA model- `scenarios: Inputs to the CEA model for each of the scenarios considered for the years 2018 and 2020
0_daysim_binaries: Daysim binaries required to run the CEA radiation script. These are included in the CEA installation package.
plots: Plots included in the paper, which are generated in thefull_paper_workflow.ipnybnotebook.
./
├── full_paper_workflow.ipynb # Jupyter notebook with the code and documentation to replicate this study
├── meter_data #
│ └── cooling_kWh_2018-2020.csv # csv file containing the hourly cooling demand by for each building for the period 2018-2020
│ └── electricity_kWh_2018-2020.csv # csv file containing the hourly electricity demand by for each building for the period 2018-2020
│ └── occupancy_wifi_2018-2020.csv # csv file containing the hourly number of devices connected to the WiFi network by building for the period 2018-2020
│ └── weather_2018_and_2020.csv # csv file containing measured weather station data for the years 2018 and 2020
├── CEA_model # directory containing the CEA models developed in this study; please refer to the for the documentation of each individual file
│ ├── baseline # baseline model of the university campus using archetypal CEA inputs for the years 2018 and 2020
│ │ ├── 2018 #
│ │ │ └── inputs #
│ │ │ └── building-geometry #
│ │ │ │ └── site.shp, site.* # shapefile containing a polygon showing the extents of the site under consideration
│ │ │ │ └── surroundings.shp # shapefile of the footprints and building heights of buildings surrounding the site
│ │ │ │ └── zone.shp, zone.* # shapefile of the footprints and building heights of buildings within the site
│ │ │ └── building-properties #
│ │ │ │ └── air_conditioning.dbf # dbf file containing the properties of the HVAC systems in each building
│ │ │ │ └── architecture.dbf # dbf file containing the architectural properties of each building (e.g., U-values, window-to-wall ratios, etc.)
│ │ │ │ └── indoor_comfort.dbf # dbf file containing the operating parameters of the HVAC systems in each building
│ │ │ │ └── internal_loads.dbf # dbf file containing the various internal gains in each building
│ │ │ │ └── supply_systems.dbf # dbf file containing the properties of the systems supplying each building
│ │ │ │ └── typology.dbf # dbf file containing the functional mix and typology of each building
│ │ │ │ └── schedules #
│ │ │ │ └── *.csv # csv file containing the operating schedules of a given building in the case study area
│ │ │ └── networks #
│ │ │ │ └── streets.shp, streets.* # shapefile of the streets within the site (not used in this study)
│ │ │ └── technology # CEA databases imported when creating a CEA model; please refer to the CEA documentation for the documentation of each individual file
│ │ │ │ └── archetypes #
│ │ │ │ └── assemblies #
│ │ │ │ └── components #
│ │ │ └── topography #
│ │ │ │ └── terrain.tiff # digital elevation model of the case study area (assumed flat in this case study)
│ │ │ └── weather #
│ │ │ └── weather.epw # weather file used in this scenario
│ │ │ └── future_scenarios # weather files for future climatic scenarios
│ │ │ └── weather-TMY.epw # typical meteorological year weather file
│ │ │ └── weather-2020.epw # weather file generated by using `weather-TMY.epw` as an input to the R package epwshiftr
│ │ │ └── weather-2040.epw # weather file generated by using `weather-TMY.epw` as an input to the R package epwshiftr
│ │ │ └── weather-2060.epw # weather file generated by using `weather-TMY.epw` as an input to the R package epwshiftr
│ │ ├── 2020 #
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ ├── calibration # calibrated CEA model of the university campus for the years 2018 and 2020
│ │ ├── 2018 #
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ ├── 2020 #
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ ├── first_run # calibration assessment metrics for the first run of the calibration procedure
│ │ │ ├── cvrmse_min.csv # minimum CV(RMSE) of the modeled total cooling (QC_sys_kWh), space cooling (Qcs_sys_kWh), and electricity demand (E_sys_kWh) along with the combination of input parameters that leads to that CV(RMSE) for each building
│ │ │ ├── nmbe_min.csv # minimum NMBE of the modeled total cooling (QC_sys_kWh), space cooling (Qcs_sys_kWh), and electricity demand (E_sys_kWh) along with the combination of input parameters that leads to that NMBE for each building
│ │ ├── second_run # calibration assessment metrics for the second run of the calibration procedure
│ │ │ └── ... # (see .\CEA_model\calibration\first_run for file description)
│ ├── regression # calibrated CEA model of the university campus after using the regression model for electricity for the years 2018 and 2020
│ │ ├── 2018 #
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ ├── 2020 #
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── regression_results_with_10-Fold_CV.csv# regression parameters and evaluation of the regression model adequacy for each building
│ ├── baseline_calibration_regression_comparison# summary of the comparison metrics for each of the versions of the CEA model
│ │ └── comparison_CVRMSE_Cooling.csv
│ │ └── comparison_CVRMSE_Electricity.csv
│ │ └── comparison_NMBE_Cooling.csv
│ │ └── comparison_NMBE_Electricity.csv
│ ├── scenarios # CEA model for each of the scenarios considered
│ │ └── baseline # scenario with 0% working from home (WFH) -- i.e., 100% occupancy -- and normal building operation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.25 # scenario with 75% occupancy and normal building operation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.5 # scenario with 50% occupancy and normal building operation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.75 # scenario with 25% occupancy and normal building operation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.25-Ns # scenario with 75% occupancy and occupant-driven building controls
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.5-Ns # scenario with 50% occupancy and occupant-driven building controls
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.75-Ns # scenario with 25% occupancy and occupant-driven building controls
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.25-closed_buildings # scenario with 75% occupancy and elastic space allocation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.5-closed_buildings # scenario with 50% occupancy and elastic space allocation
│ │ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ │ └── WFH-0.75-closed_buildings # scenario with 25% occupancy and elastic space allocation
│ │ └── ... # (see .\CEA_model\baseline\2018 for folder structure)
│ └── 0_daysim_binaries # Daysim binaries required to run the CEA radiation script
└── plots # plots generated using full_paper_workflow.ipynb
Please refer to the CEA documentation for the description of the contents of each file in a CEA case study and each file in the CEA database. Please refer to the epwshiftr GitHub page for documentation on that specific package.
This workflow requires a few different packages to be installed, most importantly the City Energy Analyst v3.27.0. CEA includes the majority of the packages required to replicate this work, except a few additional ones:
- City Energy Analyst v3.27.0
sklearnholidaysjuly
Please cite this compendium as:
@article{mosteiro_romero_2022,
title={Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization},
author={Mart\'in Mosteiro-Romero and Clayton Miller and Adrian Chong and Rudi Stouffs},
journal={Submitted to Building and Environment},
}
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