Solar energy forecasting and optimization system for efficient renewable energy integration

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dc.contributor.authorManjarres, Diana
dc.contributor.authorAlonso, Ricardo
dc.contributor.authorGil-Lopez, Sergio
dc.contributor.authorLanda-Torres, Itziar
dc.contributor.editorKramer, Oliver
dc.contributor.editorMadnick, Stuart
dc.contributor.editorWoon, Wei Lee
dc.contributor.editorAung, Zeyar
dc.contributor.institutionIA
dc.contributor.institutionSISTEMAS FOTOVOLTAICOS
dc.contributor.institutionTecnalia Research & Innovation
dc.date.accessioned2024-07-24T11:56:45Z
dc.date.available2024-07-24T11:56:45Z
dc.date.issued2017
dc.descriptionPublisher Copyright: © Springer International Publishing AG 2017.
dc.description.abstractSolar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.en
dc.description.sponsorshipAcknowledgment. This work has been supported in part by the ELKARTEK program of the Basque Government (BID3ABI project), and EMAITEK funds granted by the same institution.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationManjarres , D , Alonso , R , Gil-Lopez , S & Landa-Torres , I 2017 , Solar energy forecasting and optimization system for efficient renewable energy integration . in O Kramer , S Madnick , W L Woon & Z Aung (eds) , Data Analytics for Renewable Energy Integration : Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10691 LNAI , Springer Verlag , pp. 1-12 , 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 , Skopje , Macedonia, The Former Yugoslav Republic of , 22/09/17 . https://doi.org/10.1007/978-3-319-71643-5_1
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-319-71643-5_1
dc.identifier.isbn9783319716428
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11556/2659
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85036664509&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofData Analytics for Renewable Energy Integration
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.projectIDEusko Jaurlaritza
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsOptimization
dc.subject.keywordsPV energy forecast
dc.subject.keywordsRenewable energy integration
dc.subject.keywordsSolar energy
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.titleSolar energy forecasting and optimization system for efficient renewable energy integrationen
dc.typeconference output
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