This repository contains code and data to replicate the results in "Net Load Forecasting using Different Aggregation Levels".

Maximilian Beichter, Kaleb Phipps, Martha Maria Frysztacki, Ralf Mikut, Veit Hagenmeyer and Nicole Ludwig (2022). “Net Load Forecasting using Different Aggregation Levels”. In: Energy Informatics. doi: 10.1186/s42162-022-00213-8

The authors acknowledge support by the state of Baden-Württemberg through bwHPC.

We acknowledge the use of Diebold Mariano test from John Tsang (https://github.com/johntwk/Diebold-Mariano-Test).

This work is funded by the German Research Foundation (DFG) as part of the Research Training Group 2153 “Energy Status Data – Informatics Methods for its Collection, Analysis and Exploitation”, by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI, and the Helmholtz Association under the Program “Energy System Design”. Nicole Ludwig acknowledges funding by the DFG under Germany’s Excellence Strategy – EXC number 2064/1 – Project number 390727645.

The weather data used in this study are ERA5 reanalysis data and openly available via the Copernicus Climate Data Store (CDS) https://cds.climate.copernicus.eu/home .

The PyPSA-Eur Data is available direct in the folder "PyPSA Data". To combine this data with our feature generation you need to launch the forecasting framework with the use of the ERA5 weather data.

This code is licensed under the MIT License.