GIS-based bdew SLP calculation for german residential and nonresidential buildings

Description

Package to generate heat and electricity timeseries with demandlib based on LOD2 database

• Date: 02.10.2025
• Project: https://www.enargus.de/pub/bscw.cgi/?op=enargus.eps2&q=HEAT2Q&v=10&id=8909299

Workflow

Here you can see the overall workflow of generating building specific load profiles for heat and electricity:

Timeseries calcualtion

The following listed attributes from demandlib have to be specified:

Attributes from demandlib:
| holidays: holidays from workalender python package according to defined
| year: year
| temperature: temperature timeseries from weather data file,
| shlp_type: "bdew_profile" according to mapping in building_class_mapping.csv,
| wind_class: 1,
| annual_heat_demand: "ann_demands_per_type" calculated,
| building_class: 'building_class' from shape file,
| name: "bdew_profile" according to mapping in building_class_mapping.csv,
| ww_incl: True, includes hot water

Description of timeseries generation according to demandlib:

Heat profiles are created according to the approach described in the corresponding BDEW guideline.

The method was originally established in this PhD Thesis at TU Munich <https://mediatum.ub.tum.de/doc/601557/601557.pdf>.

The approach for generating heat demand profiles is described in section 4.1 (Synthetic load profile approach).

$$ Q_{day}(\theta) = KW \cdot h(\theta) \cdot F \cdot SF $$

| KW: Kundenwert (customer value). Daily consumption of customer at $\approx 8 ^\circ C$, depending on SLP type and Temperature timeseries.
| h: h-Wert (h-value) , depending on SLP type and daily mean temperature.
| F: Wochentagsfaktor (week day factor), depending on SLP type and day of the week.
| T: Daily mean temperature 2 meters above the ground (simple mean or "geometric series", which means a weighted sum over the previous days).
| SF: Stundenfaktor (hour factor)

The geometric series approach is meant to account for thermal inertia.

$$ \theta = \frac{T_t + 0.5 \cdot T_{t-1} + 0.25 \cdot T_{t-2} + 0.125 \cdot T_{t-3}}{1 + 0.5 + 0.25 + 0.125} $$

Depending on the profile type, different coefficients A, B, C, D for the sigmoid function are used.

$$ h(\theta) =\frac{A}{1+(\frac{B}{\theta-\theta_0})^C} + D $$

$$ \theta_0 = 40^\circ C $$

Types of houses:

| EFH: Single family house
| MFH: Multi family house
| GMK: Meetal and automotive
| GHA: Retail and wholesale
| GKO: Local authorities, credit institutions and insurance companies
| GBD: Other operational services
| GGA: Restaurants
| GBH: Accommodation
| GWA: Llaundries, dry cleaning
| GGB: Horticulture
| GBA: Bakery
| GPD: Paper and printing
| GMF: Household-like business enterprises
| GHD: Total load profile Business/Commerce/Services

Building class:

The parameter building_class (German: Baualtersklasse) can assume values in the range 1-11.

The electrical profiles are the standard load profiles from BDEW. All profiles have a resolution of 15 minutes. They are based on measurements in the German electricity sector. There is a dynamic function (h0_dyn) for the houshold (h0) profile that better takes the seasonal variance into account.

$$ F_t = -3,92\cdot10^{-10} \cdot t^4 + 3,2\cdot10^{-7} \cdot t^3– 7,02\cdot10^{-5}\cdot t^2 + 2,1\cdot10^{-3}\cdot t + 1,24 $$

With t the day of the year as a decimal number.

The following profile types are available. Be aware that the types in Python code are strings in lowercase.

| G0:, "General trade/business/commerce", "Weighted average of profiles G1-G6"
| G1:, "Business on weekdays 8 a.m. - 6 p.m.", "e.g. offices, doctors' surgeries, workshops, administrative facilities"
| G2:, "Businesses with heavy to predominant consumption in the evening hours", "e.g. sports clubs, fitness studios, evening restaurants"
| G3:, "Continuous business", "e.g. cold stores, pumps, sewage treatment plants"
| G4:, "Shop/barber shop"
| G5:, "Bakery with bakery"
| G6:, "Weekend operation", "e.g. cinemas"
| G7:, "Mobile phone transmitter station", "continuous band load profile"
| L0:, "General farms", "Weighted average of profiles L1 and L2"
| L1:, "Farms with dairy farming/part-time livestock farming",
| L2:, "Other farms",
| H0/H0_dyn:, "Household/dynamic houshold",

Installation

1. Create and activate enviroment. Example with anaconda: conda activate bdewles
2. cd to folder with github clone
3. pip install -e .

Usage

1. cd "folder/main.py"
2. Store necessary GIS data to "folder/data/raw" and modify overwrite_shp.py accordingly to adapt the columns of the shapefile
3. Prepare shp.-file with prepare_for_bdew.py: this scripts adapts the shapefile using the functions in datamgmt to assign building age, building type etc.
4. python main.py
5. Parameters can be changed in "datamgmt/Parameters.py"
6. Results will be stored in "results".

Census file format

Needs a csv file that links the GITTER_ID_ item to the census statistics. For an example of the file, see ./data/census/2024-08-12_zensus.csv.

Shapefile input format

Needs to be a shapefile with columns: ['GML_ID', 'USE_CALC', 'AREA_CALC', 'VOL_CALC', 'HEIGH_MEAS', 'HEIGH_CALC', 'CONSTRUCTI', 'CENS_GRID', 'geometry'] If not, change with the script in ./data/raw-data/overwrite_shp.py.

Support

• Post an issue
• Contact the authors

Contributing

• Open a detailed pull request and email the authors.

Authors and acknowledgment

• Lennart Trentmann (lennart.trentmann@tum.de)
• Amedeo Ceruti (amedeo.ceruti@tum.de)
• Benedikt Schweiger (benedikt.schweiger@tum.de)

Acknowledgement: the Heat2Q project partners for their ongoing feedback.

Project status

In development until early 2026.