Model Input - Data Files

Time series data, as well as information about buildings and districts, are provided to the District Energy Model (DEM) through data files. Most of these files are stored in Feather format (Apache Software Foundation, 2025).

This section describes the required files, their contents, and the expected data formats. Although all files must be present for the simulation to run, their entries can be set to zero if a dataset is not relevant to a specific case (for example, the wind power file if wind generation is excluded from the scenario).

Note

A full Switzerland-wide data package for DEM simulations is currently in preparation and will be made available soon. This will eliminate the need for users to assemble their own datasets. In the meantime, please reach out to us (Contact) to request access to the data.

All input files must be located within the data directory following the predefined directory structure (see below).

District_Energy_Model/
├── config/
│   ├── ...
│   └── ...
├── data/
│   ├── community_data/
│   │   └── ...
│   ├── electricity_demand/
│   │   └── ...
│   ├── electricity_mix_national/
│   │   └── electricity_mix.feather
│   ├── heat_demand/
│   │   └── DHW_Profile.feather
│   ├── master_data/
│   │   ├── HDD_and_HDH_profiles/
│   │   │   ├── HDD_Municipality_2023.feather
│   │   │   ├── HDD_Municipality_2030.feather
│   │   │   ├── HDD_Municipality_2040.feather
│   │   │   ├── HDD_Municipality_2050.feather
│   │   │   └── ...
│   │   └── simulation_data/
│   │       ├── df_master_sim.feather
│   │       ├── meta_file_2.feather
│   │       ├── simulation_profiles_file.feather
│   │       └── ...
│   ├── tech_wind_power/
│   │   ├── profiles/
│   │   │   └── ...
│   │   └── p_installed_kW_wind_power.feather
│   └── weather_data/
│       └── com_files/
│           └── ...
├── doc/
│   ├── ...
│   └── ...
├── src/
│   ├── ...
│   └── ...
├── LICENSE.txt
└── README.md

Master File

This file contains information about each individual building, with one row per building. District-level information is derived from this file through aggregation. The file may include data for multiple districts; during a simulation, only the buildings belonging to the specified district are selected.

Each building is identified by a unique identification number (column 0: EGID) and assigned to a municipality represented by a numerical code (column 4: GGDENR). Custom districts can also be analyzed by specifying the buildings included in the district via their EGID values.

For Switzerland, data in columns 0 to 15 is sourced from the Federal Register of Buildings and Dwellings (RBD) (Federal Statistical Office, 2025), and the corresponding column naming conventions have been adopted accordingly.

File name	df_master_sim
Format	.feather
Directory	./data/master_data/simulation_data/
Index	Numeric (integer), 0 to X, where X is the number buildings contained in the file.
Number of rows	According to number of buildings.
Number of columns	49

Number	Column	Data type	Description
0	EGID	int64
1	GDEKT	object
2	GKAT	float64
3	GKLAS	float64
4	GGDENR	int64
5	GGDENAME	object
6	GKODE	float64
7	GKODN	float64
8	GBAUJ	float64
9	GBAUP	float64
10	GAREA	float64
11	GASTW	float64
12	GENH1	float64
13	GENH2	float64
14	GENW1	float64
15	GENW2	float64
16	Coord_lat	float64
17	Coord_long	float64
18	PV_Pot	float64
19	PV_Pot_reco	float64
20	FPV_Pot	float64
21	FPV_Pot_reco	float64
22	BeginningOfOperation	object
23	InitialPower	float64
24	TotalPower	float64
25	PlantCategory	object
26	TotalEnergy	float64
27	altitude	int32
28	Temperature_mean	float64
29	renovation_base	float64
30	renovation_low	float64
31	renovation_high	float64
32	heat_energy_demand_estimate_kWh_combined	float64
33	dhw_estimatin_kWh_combined	float64
34	cluster_number	int64
35	kWh_household_sfh	float64
36	kWh_household_mfh	float64
37	Heating_System	object
38	Hot_Water_System	object
39	Electricity_Industry	float64
40	Electricity_Service	float64
41	s_wd_bm	float64
42	s_wet_bm	float64
43	LocalHydroPotential_Laufkraftwerk	float64
44	LocalHydroPotential_Speicherkraftwerk	float64
45	LocalHydroPotential_Pumpspeicherkraftwerk	float64
46	avg_dh_connection_distance	float64
47	dh_distance_cat	int64
48	heat_energy_demand_renov_estimate_kWh	float64

Meta file

This file contains information about each district, with one row per district. Some of the data can be derived as an aggregate from the Master File.

The district identification number (column 1: GGDENR) must match the corresponding municipality code of the buildings listed in the Master File.

Number	Column	Data type	Description
0	Municipality	object
1	GGDENR	int64
2	Canton	object
3	Coord_lat_median	float64
4	Coord_long_median	float64
5	altitude_median	float64
6	Filename	object
7	LocalHydroPotential	float64
8	LocalHydroPotential_Laufkraftwerk	float64
9	LocalHydroPotential_Speicherkraftwerk	float64
10	LocalHydroPotential_Pumpspeicherkraftwerk	float64
11	v_h_eh	float64
12	v_h_hp	float64
13	v_h_dh	float64
14	v_h_gb	float64
15	v_h_ob	float64
16	v_h_wb	float64
17	v_h_solar	float64
18	v_h_other	float64
19	Total_Heating	float64
20	v_hw_eh	float64
21	v_hw_hp	float64
22	v_hw_dh	float64
23	v_hw_gb	float64
24	v_hw_ob	float64
25	v_hw_wb	float64
26	v_hw_solar	float64
27	v_hw_other	float64
28	Total_Hot_Water	float64
29	PV_Pot	float64
30	TotalEnergy	float64
31	kWh_household_sfh	float64
32	kWh_household_mfh	float64
33	Electricity_Industry	float64
34	Electricity_Service	float64
35	s_wd_bm	float64
36	s_wet_bm	float64
37	PV_Filename	object
38	dh_cap_class_1	float64
39	dh_cap_class_2	float64
40	dh_cap_class_3	float64
41	dh_cap_renov_class_1	float64
42	dh_cap_renov_class_2	float64
43	dh_cap_renov_class_3	float64
44	dh_avg_dist_class_1	float64
45	dh_avg_dist_class_2	float64
46	dh_avg_dist_class_3	float64
47	m_per_kWh_class_1_renov	float64
48	m_per_kWh_class_2_renov	float64
49	m_per_kWh_class_3_renov	float64
50	m_per_kWh_class_1	float64
51	m_per_kWh_class_2	float64
52	m_per_kWh_class_3	float64

Simulation profiles

This file contains hourly profiles for an entire year (i.e., 8760 hours) representing various generation and demand metrics at national and regional levels.

File name	simulation_profiles_file
Format	.feather
Directory	./data/master_data/simulation_data/
Index	Numeric (integer), 0 to 8759, hour of the year
Number of rows	8760
Number of columns	51

Number	Column	Data type	Description
0	Woody_Biomass_Profile	float64
1	Wet_Biomass_Profile	float64
2	Electricity_Profile_MFH	float64
3	Electricity_Profile_SFH	float64
4	Electricity_Profile_Industry_AG	float64
5	Electricity_Profile_Industry_FR	float64
6	Electricity_Profile_Industry_GL	float64
7	Electricity_Profile_Industry_GR	float64
8	Electricity_Profile_Industry_LU	float64
9	Electricity_Profile_Industry_NE	float64
10	Electricity_Profile_Industry_SO	float64
11	Electricity_Profile_Industry_SG	float64
12	Electricity_Profile_Industry_TI	float64
13	Electricity_Profile_Industry_TG	float64
14	Electricity_Profile_Industry_VS	float64
15	Electricity_Profile_Industry_AI	float64
16	Electricity_Profile_Industry_AR	float64
17	Electricity_Profile_Industry_BL	float64
18	Electricity_Profile_Industry_BS	float64
19	Electricity_Profile_Industry_BE	float64
20	Electricity_Profile_Industry_JU	float64
21	Electricity_Profile_Industry_SZ	float64
22	Electricity_Profile_Industry_ZG	float64
23	Electricity_Profile_Industry_OW	float64
24	Electricity_Profile_Industry_NW	float64
25	Electricity_Profile_Industry_UR	float64
26	Electricity_Profile_Industry_GE	float64
27	Electricity_Profile_Industry_VD	float64
28	Electricity_Profile_Industry_SH	float64
29	Electricity_Profile_Industry_ZH	float64
30	Hydro_Lokal_Laufwasser_Profile	float64
31	Hydro_Lokal_Speicher_Profile	float64
32	Hydro_Lokal_Pumpspeicher_Profile	float64
33	PV_Profile_0	float64
34	PV_Profile_1	float64
35	PV_Profile_2	float64
36	PV_Profile_3	float64
37	PV_Profile_4	float64
38	PV_Profile_5	float64
39	PV_Profile_6	float64
40	PV_Profile_7	float64
41	PV_Profile_8	float64
42	PV_Profile_9	float64
43	PV_Profile_10	float64
44	Hydro_National_Profile	float64
45	Nuclear_National_Profile	float64
46	Solar_National_Profile	float64
47	Wind_National_Profile	float64
48	Biomass_National_Profile	float64
49	Other_National_Profile	float64
50	Import_National_Profile	float64

Temperatures

These files contains hourly temperature data for an entire year (i.e., 8760 hours) across multiple years (i.e., one column per year). One file must be provided per district, where the temperature values represent spatial averages over the district area.

Past years include historical measurements from monitoring stations, while future years contain projected values based on climate scenarios (see also Climate Adjustment).

File name	[com_nr], e.g. “2762” for municipality of Allschwil
Format	.feather
Directory	./data/weather_data/
Index	datetime format; hourly entries; e.g., 2016-01-01 00:00:00 to 2016-12-31 23:00:00
Number of rows	8760
Number of columns	according to number of captured years

Number	Column	Data type	Description
0	2016	float64
1	2023	float64
2	2024	float64
3	2025	float64
4	2030	float64
5	2040	float64
6	2050	float64

DHW profile

This file contains an hourly domestic hot water (DHW) demand profile for an entire year (i.e., 8760 hours). The profile is normalised to 1 and can be scaled according to the annual DHW demand of the district or building.

File name	DHW_Profile
Format	.feather
Directory	./data/heat_demand/
Index	Numeric (integer), 0 to 8759, hour of the year
Number of rows	8760
Number of columns	1

Number	Column	Data type	Description
0	DHW_Profile	float64

Wind power capacity

This file contains the currently installed wind power capacity (in kW) for each municipality.

File name	p_installed_kW_wind_power
Format	.feather
Directory	./data/tech_wind_power/
Index	Numeric (integer), 0 to X, where X is the number districts contained in the file.
Number of rows	According to number of districts.
Number of columns	2

Number	Column	Data type	Description
0	Municipality	object
1	p_kW	float64

Wind power profiles

These files contain normalised hourly wind power generation profiles aggregated at the municipal level. For each municipality, two files are provided: one representing profiles optimised for maximum annual generation, and another optimised for maximum winter generation.

For Switzerland, the Wind-Topo dataset is used (Dujardin and Lehning, 2022a and 2022b).

in progress

National electricity mix

This file contains hourly profiles of the national electricity mix. The data include the hourly contribution of each generation technology and are used to create normalised profiles of national electricity generation technologies.

File name	electricity_mix
Format	.feather
Directory	./data/electricity_mix_national/
Index	Numeric (integer), 0 to 8759, hour of the year
Number of rows	8760
Number of columns	10

Number	Column	Data type	Description
0	Hydro Pumpspeicher	float64
1	Hydro Laufwasser	float64
2	Hydro Speicher	float64
3	Nuclear	float64
4	Solar	float64
5	Wind	float64
6	Biomass	float64
7	Other	float64
8	Load	float64
9	Import	float64

HDD Profiles

These files contain the number of heating degree days (HDD) per year for each municipality, calculated for base temperatures of 12 °C and 15 °C. Each file corresponds to one simulation year, and each row represents a single municipality.

File name	HDD_Municipality_[year]
Format	.feather
Directory	./data/master_data/HDD_and_HDH_profiles/
Index	Numeric (integer), 0 to X, where X is the number districts contained in the file.
Number of rows	According to number of districts.
Number of columns	4

Number	Column	Data type	Description
0	Municipality	object
1	HDD_12_2023	float64
2	HDD_15_2023	float64
3	GGDENR	int64

EV demand profiles

For Switzerland, the dataset by Herrera and Hug (2025a; 2025b) is used to model hourly EV charging demand and daily flexibility.

in progress

References

Apache Software Foundation. (2025). Feather file format (Apache Arrow). https://arrow.apache.org/docs/python/feather.html

Dujardin, J., Lehning, M. (2022a). Wind-Topo_model. EnviDat. https://www.doi.org/10.16904/envidat.301.

Dujardin, J., & Lehning, M. (2022b). Wind‐Topo: Downscaling near‐surface wind fields to high‐resolution topography in highly complex terrain with deep learning. Quarterly Journal of the Royal Meteorological Society, 148(744), 1368-1388. https://doi.org/10.1002/qj.4265

Parajeles Herrera, M., & Hug, G. (2025a). Charging Demand and Flexibility Bounds for Large-Scale BEV Fleets - The Case Study of Switzerland [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16597426

Herrera, M. P., & Hug, G. (2025b). Modeling Charging Demand and Quantifying Flexibility Bounds for Large-Scale BEV Fleets. arXiv preprint arXiv:2504.03633. https://doi.org/10.48550/arXiv.2504.03633

Federal Statistical Office (FSO). (2025). Federal register of buildings and dwellings (RBD). https://www.bfs.admin.ch/bfs/en/home/registers/federal-register-buildings-dwellings.html