This notebook is a contribution to the project Open Power System Data. Open Power System Data develops a platform for free and open data for electricity system modeling. We collect, check, process, document, and provide data that are publicly available but currently inconvenient to use. More info on Open Power System Data:
This file is a Jupyter Notebook. A Jupyter Notebook is a file that combines executable programming code with visualizations and comments in markdown format, allowing for an intuitive documentation of the code. We use Jupyter Notebooks for combined coding and documentation. We use Python 3 as programming language. All Notebooks are stored on GitHub, a platform for software development, and are publicly available. More information on our IT-concept can be found here. See also our step-by-step manual how to use the dataplatform.
This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for several European countries and years in an hourly resolution.
Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating.
COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements.
All data processing as well as the download of relevant input data is conducted in python and pandas and has been documented in the processing notebooks linked below. Please also consider and cite our Data Descriptor.
A complete list of data sources is provided on the datapackage information website. They are also contained in the JSON file that contains all metadata.
import pandas as pd; pd.read_csv('input/notation.csv', index_col=list(range(4)))
This notebook as well as all other documents in this repository is published under the MIT License.