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.