Schreiber, Jens0000-0002-9979-80532022-07-142022-07-14https://daks.uni-kassel.de/handle/123456789/4510.48662/daks-11Photovoltaic and wind power forecasts in power systems with a high share of renewable energy are essential in several applications. These include stable grid operation, profitable power trading, and forward-looking system planning. However, there is a lack of publicly available datasets for research on machine learning based prediction methods. This dataset provides an openly accessible time series dataset, as detailed in https://doi.org/10.48550/arXiv.2204.00411, with realistic synthetic power data. Other publicly and non-publicly available datasets often lack precise geographic coordinates, timestamps, or static power plant information, e.g., to protect business secrets. On the opposite, this dataset provides these. The dataset comprises 120 photovoltaic and 273 wind power plants with distinct sides all over Germany from 500 days in hourly resolution. This large number of available sides allows forecasting experiments to include spatial correlations and run experiments in transfer and multi-task learning. It includes side-specific, power source-dependent, non-synthetic input features from the ICON-EU weather model. A simulation of virtual power plants with physical models and actual meteorological measurements provides realistic synthetic power measurement time series. These time series correspond to the power output of virtual power plants at the location of the respective weather measurements. Since the synthetic time series are based exclusively on weather measurements, possible errors in the weather forecast are comparable to those in actual power data. We are incredibly thankful to Enercon (www.enercon.de) for providing information on turbine characteristics. We are also grateful to the German weather service - DWD (www.dwd.de), for providing an open and excellent API to access weather forecasts. IMPORTANT: In case you use the data please cite our corresponding article https://doi.org/10.48550/arXiv.2204.00411.engCreative Commons Attribution 4.0https://creativecommons.org/licenses/by/4.0/Wind Power ForecastingPhotovoltaic Power ForecastingSynthetic DatasetMachine LearningTransfer Learning408-01 Elektronische Halbleiter, Bauelemente und Schaltungen, Integrierte Systeme409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung621.3004DatasetVogt, Stephan0000-0002-3230-8822Bundesministerium für Bildung und Forschung – BMBF04pz7b180