This notebook is part of 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.
The main focus of this datapackage is German data, but we include data from other countries wherever possible. The timeseries become available at different points in time depending on the sources. The full dataset is only available from 2012 onwards. The data has been downloaded from the sources, resampled and merged in a large CSV file with hourly resolution. Additionally, the data available at a higher resolution (some renewables in-feed, 15 minutes) is provided in a separate file.
import pandas as pd; pd.read_csv('input/notation.csv', index_col=list(range(4)))
|ISO-2 digit country code and name of balancing area if applicable, eg. DE-amprion||load||load||Hourly Load from ENTSO-E Data Portal|
|price||EPEX||Day-ahead spot price price from EPEX spot|
|Elspot||Day-ahead spot price price from NORDPOOL spot|
|solar / wind-onshore / wind-ofshore||generation||Electricity produced py solar power plants|
|forecast||Day-ahead generation forecast|
|capacity||Installed capacity (actual availability not accounted for)|
|profile||Share of installed capacity producing|