Synthetic Photovoltaic and Wind Power Forecasting Data

{"last":"Schreiber","first":"Jens","affiliation":"University of Kassel, Intelligent Embedded System","id":"orcid","id_value":"0000-0002-9979-8053"}

dc.contributor	{"last":"Vogt","first":"Stephan","role":"Producer","affiliation":"University of Kassel, Intelligent Embedded System","id":"orcid","id_value":"0000-0002-3230-8822"}
dc.contributor.author	{"last":"Schreiber","first":"Jens","affiliation":"University of Kassel, Intelligent Embedded System","id":"orcid","id_value":"0000-0002-9979-8053"}
dc.date.accessioned	2022-07-14T14:08:53Z
dc.date.available	2022-07-14T14:08:53Z
dc.identifier.uri	https://daks.uni-kassel.de/handle/123456789/45
dc.identifier.uri	http://dx.doi.org/10.48662/daks-11
dc.description	Photovoltaic 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.	de_DE
dc.description.sponsorship	{"funderName":"BMBF - Bundesministerium für Bildung und Forschung","awardTitle":"TRANSFER: Transfer Learning als essentielles Werkzeug für die Energiewende","awardNumber":"01IS20020B"}
dc.language.iso	eng	de_DE
dc.relation	{"relationType":"IsSupplementTo","relatedIdentifierType":"DOI","id_value":"https://doi.org/10.48550/arXiv.2204.00411"}
dc.relation	{"relationType":"IsSupplementTo","relatedIdentifierType":"arXiv","id_value":"2204.00411"}
dc.rights	Creative Commons Attribution 4.0
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Wind Power Forecasting	de_DE
dc.subject	Photovoltaic Power Forecasting	de_DE
dc.subject	Synthetic Dataset	de_DE
dc.subject	Machine Learning	de_DE
dc.subject	Transfer Learning	de_DE
dc.subject.classification	408-01 Elektronische Halbleiter, Bauelemente und Schaltungen, Integrierte Systeme	de_DE
dc.subject.classification	409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung	de_DE
dc.subject.ddc	621.3
dc.subject.ddc	004
dc.title	Synthetic Photovoltaic and Wind Power Forecasting Data	de_DE
dc.type	Dataset	de_DE
dc.type	Software	de_DE
local.ka.faculty	FB16:Elektrotechnik/Informatik	de_DE
local.ka.department	University of Kassel, Intelligent Embedded System	de_DE