prepare_heat_demand¶
Inputs¶
- in_path1str
raw/weatherdata
: path of input directory with weather data- in_path2str
raw/distribution_households.csv
: path of input file with household distributions data as .csv- in_path3str
raw/holidays.csv
: path of input file with holidays of all states in Germany as .csv- in_path4str
raw/building_class.csv
: path of input file with building classes of all states in Germany as .csv- in_path5str
raw/scalars/demands.csv
: path of scalar data as .csv- out_path1str
results/_resources/scal_load_heat.csv
: path of output file with aggregated scalar data as .csv- out_path2str
results/_resources/ts_load_heat.csv
: path of output file with timeseries data as .csv- logfilestr
results/_resources/load_heat.log
: path to logfile
Outputs¶
- pandas.DataFrame
with grouped and aggregated data of heat demands. Data is grouped by region, energy source, technology and chp capability and contains net capacity and efficiency.
Description¶
The script produces heat demand profiles using the demandlib. For this purpose, it reads the scalar input data and filters them according to the corresponding heat demand. By processing historical weather data as well as a household distribution in single- and multi-family houses that is assumed to be constant and the holidays belonging to the evaluated year, demandlib creates heat profiles, which are additionally normalized. Since the consumers trade, commerce and services (german: Gewerbe, Handel und Dienstleistungen (ghd)) and private household (hh) are processed individually by demandlib, they are also passed individually in the scalar input data. The script summarizes the respective demand of the consumers and stores it in scalar resources.