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Short-term load forecasting of electricity demand for the residential sector based on modelling techniques: A systematic review

authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Mecânica
dc.contributor.authorRodrigues, Filipe
dc.contributor.authorCardeira, Carlos
dc.contributor.authorCalado, João
dc.contributor.authorMelício, Rui
dc.date.accessioned2025-08-01T09:35:40Z
dc.date.available2025-08-01T09:35:40Z
dc.date.issued2023-05-15
dc.description.abstractIn this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.eng
dc.identifier.citationRodrigues, F., Cardeira, C., Calado, J. M. F., & Melício, R. (2023). Short-term load forecasting of electricity demand for the residential sector based on modelling techniques: A systematic review. Energies, 16(10), 1-26. https://doi.org/10.3390/en16104098
dc.identifier.doihttps://doi.org/10.3390/en16104098
dc.identifier.eissn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.21/22004
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationUIDB/50022/2020
dc.relation.hasversionhttps://www.mdpi.com/1996-1073/16/10/4098
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSTLF
dc.subjectElectricity
dc.subjectResidential (household)
dc.subjectArtificial intelligence
dc.subjectEnergy demand
dc.subjectModelling techniques
dc.subjectHour-ahead load
dc.titleShort-term load forecasting of electricity demand for the residential sector based on modelling techniques: A systematic revieweng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage26
oaire.citation.issue10
oaire.citation.startPage1
oaire.citation.titleEnergies
oaire.citation.volume16
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameCalado
person.givenNameJoão
person.identifier370725
person.identifier.ciencia-idB518-93E3-E7AB
person.identifier.orcid0000-0001-6628-4657
person.identifier.ridM-4167-2013
person.identifier.scopus-author-id7006897277
relation.isAuthorOfPublication602b1546-f4f1-4cd5-8d29-d835d54c9bd6
relation.isAuthorOfPublication.latestForDiscovery602b1546-f4f1-4cd5-8d29-d835d54c9bd6

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