Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives

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dc.contributor.authorDel Ser, J.
dc.contributor.authorCasillas-Perez, D.
dc.contributor.authorCornejo-Bueno, L.
dc.contributor.authorPrieto-Godino, L.
dc.contributor.authorSanz-Justo, J.
dc.contributor.authorCasanova-Mateo, C.
dc.contributor.authorSalcedo-Sanz, S.
dc.contributor.institutionIA
dc.date.issued2022-03
dc.descriptionPublisher Copyright: © 2022 Elsevier B.V.
dc.description.abstractIn the last few years, methods falling within the family of randomization-based machine learning models have grasped a great interest in the Artificial Intelligence community, mainly due to their excellent balance between performance in prediction problems and their computational efficiency. The use of these models for prediction problems related to renewable energy sources has been particularly notable in recent times, including different ways in which randomization is considered, their hybridization with other modeling techniques and/or their multi-layered (deep) and ensemble arrangement. This manuscript comprehensively reviews the most important features of randomization-based machine learning methods, and critically examines recent evidences of their application to renewable energy prediction problems, focusing on those related to solar, wind, marine/ocean and hydro-power renewable sources. Our study of the literature is complemented by an extensive experimental setup encompassing three real-world problems dealing with solar radiation prediction, wind speed prediction in wind farms and hydro-power energy. In all these problems randomization-based methods are reported to achieve a better predictive performance than their corresponding state-of-the-art solutions, while demanding a dramatically lower computational effort for its learning phases. Finally, we pause and reflect on important challenges faced by these methods when applied to renewable energy prediction problems, such as their intrinsic epistemic uncertainty, or the need for explainability. We also point out several research opportunities that arise from this vibrant research area.en
dc.description.sponsorshipThis research has been partially supported by Spanish Ministry of Science and Innovation (MICINN) , through Project Number PID2020-115454GB-C21 . This research has also been partially supported by Comunidad de Madrid , PROMINT-CM project (grant ref: P2018/EMT-4366 ). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049 ), as well as the consolidated research group MATHMODE (ref. T1294-19 ).
dc.description.statusPeer reviewed
dc.identifier.citationDel Ser , J , Casillas-Perez , D , Cornejo-Bueno , L , Prieto-Godino , L , Sanz-Justo , J , Casanova-Mateo , C & Salcedo-Sanz , S 2022 , ' Randomization-based machine learning in renewable energy prediction problems : Critical literature review, new results and perspectives ' , Applied Soft Computing Journal , vol. 118 , 108526 . https://doi.org/10.1016/j.asoc.2022.108526
dc.identifier.doi10.1016/j.asoc.2022.108526
dc.identifier.issn1568-4946
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85124176894&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Soft Computing Journal
dc.relation.projectIDComunidad de Madrid, P2018/EMT-4366
dc.relation.projectIDEusko Jaurlaritza, T1294-19-KK-2020/00049
dc.relation.projectIDMinisterio de Ciencia e Innovación, MICINN, PID2020-115454GB-C21
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsHydro-power
dc.subject.keywordsMachine Learning
dc.subject.keywordsMarine Energy
dc.subject.keywordsRandomization-based algorithms
dc.subject.keywordsRenewable resources
dc.subject.keywordsSolar Energy
dc.subject.keywordsWind energy
dc.subject.keywordsSoftware
dc.subject.keywordsSDG 7 - Affordable and Clean Energy
dc.titleRandomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectivesen
dc.typejournal article
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