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dc.contributor{"last":"Vogt","first":"Stephan","role":"Producer","affiliation":"University of Kassel, Intelligent Embedded System","id":"orcid","id_value":"0000-0002-3230-8822"}{"last":"Schreiber","first":"Jens","affiliation":"University of Kassel, Intelligent Embedded System","id":"orcid","id_value":"0000-0002-9979-8053"}
dc.descriptionPhotovoltaic 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, 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 ( for providing information on turbine characteristics. We are also grateful to the German weather service - DWD (, for providing an open and excellent API to access weather forecasts. IMPORTANT: In case you use the data please cite our corresponding article
dc.description.sponsorship{"funderName":"BMBF - Bundesministerium für Bildung und Forschung","awardTitle":"TRANSFER: Transfer Learning als essentielles Werkzeug für die Energiewende","awardNumber":"01IS20020B"}
dc.rightsCreative Commons Attribution 4.0
dc.subjectWind Power Forecastingde_DE
dc.subjectPhotovoltaic Power Forecastingde_DE
dc.subjectSynthetic Datasetde_DE
dc.subjectMachine Learningde_DE
dc.subjectTransfer Learningde_DE
dc.subject.classification408-01 Elektronische Halbleiter, Bauelemente und Schaltungen, Integrierte Systemede_DE
dc.subject.classification409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierungde_DE
dc.titleSynthetic Photovoltaic and Wind Power Forecasting Datade_DE
local.ka.departmentUniversity of Kassel, Intelligent Embedded Systemde_DE

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Creative Commons Attribution 4.0
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0