Short-term load forecasting models: A review of challenges, progress, and the road ahead

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dc.authorid0000-0002-2134-4182en_US
dc.authorid0000-0002-8082-1241en_US
dc.authorid0000-0002-6119-0886en_US
dc.authorid0000-0003-1125-3305en_US
dc.authorid0000-0002-0983-2562en_US
dc.authorid0000-0002-2388-3710en_US
dc.contributor.authorAkhtar, Saima
dc.contributor.authorShahzad, Sulman
dc.contributor.authorZaheer, Asad
dc.contributor.authorUllah, Hafiz Sami
dc.contributor.authorKılıç, Heybet
dc.contributor.authorGono, Radomir
dc.contributor.authorJasiński, Michał
dc.contributor.authorLeonowicz, Zbigniew
dc.date.accessioned2023-10-09T13:19:24Z
dc.date.available2023-10-09T13:19:24Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractShort-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.en_US
dc.identifier.citationAkhtar, S., Shahzad, S., Zaheer, A., Ullah, H. S., Kılıç, H., Gono, R. ve diğerleri. (2023). Short-term load forecasting models: A review of challenges, progress, and the road ahead. Energies, 16(10), 1-29.en_US
dc.identifier.doi10.3390/en16104060
dc.identifier.endpage29en_US
dc.identifier.issn1996-1073
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85160598295
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/1996-1073/16/10/4060
dc.identifier.urihttps://hdl.handle.net/11468/12780
dc.identifier.volume16en_US
dc.identifier.wosWOS:000996871600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKılıç, Heybet
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofEnergies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAutoregressionen_US
dc.subjectData qualityen_US
dc.subjectDecision treeen_US
dc.subjectDeep learningen_US
dc.subjectEnsemble methodsen_US
dc.subjectExponential smoothingen_US
dc.subjectHybrid modelsen_US
dc.subjectNeural networksen_US
dc.subjectRandom foresten_US
dc.subjectShort-term load forecastingen_US
dc.subjectSupport vector machinesen_US
dc.subjectTime seriesen_US
dc.titleShort-term load forecasting models: A review of challenges, progress, and the road aheaden_US
dc.titleShort-term load forecasting models: A review of challenges, progress, and the road ahead
dc.typeArticleen_US

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