GeoHeatFlex is an open-source tool for quantifying heating flexibility potential in a building stock at high spatial resolution based on heating consumption data. This model uses high resolution heating consumption data, historical temperature data, and building size data. An example case study for gas-heated homes in Britain is included, but the model can be generalized to any region where similar data is available.
GeoHeatFlex is distributed under the MIT license. Please note that the data used in this model have different licenses.
When you use GeoHeatFlex, please cite the forthcoming paper:
- Claire Halloran, Jesus Lizana, Malcolm McCulloch, Quantifying space heating flexibility potential at high spatial resolution with heating consumption data.
Clone the GeoHeatFlex repository using the following command in your terminal:
/some/other/path % cd /some/path
/some/path % git clone https://github.com/clairehalloran/GeoHeatFlex.git
Install the python dependencies using the package manager of your choice. When using conda, enter the following commands in your terminal to install and activate the environment:
.../GeoHeatFlex % conda env create -f environment.yaml
.../GeoHeatFlex % conda activate GeoHeatFlex
All input data should be put in the Data folder. To recreate the case study for gas-heated houses, include the following data:
- HadGrid-UK minimum and maximum daily temperature observations for 2010-2022 in the
Data/tasminandData/tasmaxfolders, respectively. These data are available in the CEDA Archive under the Open Government License. - DataZone boundaries 2011 shapefile folder
Data/SG_DataZoneBdry_2011available from SpatialData.gov.scot under the Open Government License. - Lower Layer Super Output Areas (LSOA) boundaries 2011
Data/Lower_Layer_Super_Output_Areas_Dec_2011_Boundaries_Full_Extent_BFE_EW_V3_2022_-4926191891001926707.geojson, available from the UK Office for National Statistics Open Geography portalx under the Open Government License. - LSOA domestic gas 2010 to 2021
Data/LSOA_domestic_gas_2010-21.xlsx, available from the UK Department for Energy Security and Net Zero, available under the Open Government License. - Energy Consumption in the UK 2022 End Use Tables
Data/ECUK_2022_End_Use_tables_27102022.xlsx, available from available from the UK Department for Energy Security and Net Zero, available under the Open Government License. - Census 2011 Table QS407EW: Number of rooms for England and Wales
Data/England_and_Wales_census_2011_number_of_rooms.csvby 2011 LSOA. This Office for National Statistics data is available on nomis under the Open Government License. - Census 2011 Table QS407S: Number of rooms for Scotland,
Data/Scotland_census_2011_number_of_rooms.xlsxby 2011 Data Zone, available from Scotland's Census under the Open Government License. Search for "QS407SC" to find this table. - Electrification of Heat trial heat pump performance cleansed data in
Data/Electrification of Heat/Dataset 1andData/Electrification of Heat/Dataset 2. These datasets are available from the UK Data Service under the Open Government License v2.0. - EPC-based thermal building characteristics for LSOAs in England and Wales by Alexandre Canet at Cardiff University in the
Data/UKERCavailable from the UKERC Energy Data Centre under a CC-BY license.
The model is run with the following workflow:
The heating degree days for the time period that heating consumption is reported are calculated in the calculate_regional_HDDs.py script.
Heat losses are calculated in the calculate_heating_losses.py script.
Heat capacity and thermal time constants are calculated in the calculate_time_constants.py script. This script also saves the total thermal energy storage capacity in each region.
The heating flexibility duration as measured by the number of comfortable heat-free hours is calculated in the calculate_flexibility_duration.py script.
Time constants based on an exponential fit of indoor temperature drop for homes in the Electrification of Heat Trial are calculated in the EoH_time_constants.py script.
The time constants calculated in GeoHeatFlex are compared with those obtained with the energy performance certificate-based method introduced by Canet and Qadrdan and the indoor temperature-based method in the validate_time_constants.py script.