Time series: Processing Notebook

This Notebook is part of the heating profiles data package contributed to Open Power System Data.

# 1. About Open Power System Data¶

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:

# 2. About Jupyter Notebooks and GitHub¶

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 buildins and the coefficient of performance (COP) of heat pumps is calculated for 16 European countries from 2008 to 2013 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, population geodata, and 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 and radiators, both combined with water heating – are calculated based on COP and heating curves, reanalysis temperature data, a spatial aggregation procedure with respect to the heat demand, and a correction procedure for part-load losses.

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 notebook linked above.

# 4. Data sources¶

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.

# 5. Naming conventions¶

In [1]:
import pandas as pd; pd.read_csv('input/notation.csv', index_col=list(range(4)))

Out[1]:
country variable attribute description
ISO-2 digit country code heat_demand total Heat demand for space and water heating
space Heat demand for space heating
water Heat demand for water heating
space_SFH Heat demand for space heating in single-family houses
space_MFH Heat demand for space heating in multi-family houses
space_COM Heat demand for space heating in commercial buildings
water_SFH Heat demand for water heating in single-family houses
water_MFH Heat demand for water heating in multi-family houses
water_COM Heat demand for water heating in commercial buildings
heat_profile space_SFH Normalized heat demand for space heating in single-family houses
space_MFH Normalized heat demand for space heating in multi-family houses
space_COM Normalized heat demand for space heating in commercial buildings
water_SFH Normalized heat demand for water heating in single-family houses
water_MFH Normalized heat demand for water heating in multi-family houses
water_COM Normalized heat demand for water heating in commercial buildings
COP ASHP_floor COP of air-source heat pumps with floor heating