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A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction

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dc.contributor.authorJallal, Mohammed Ali
dc.contributor.authorGonzález Vidal, Aurora
dc.contributor.authorSkarmeta Gómez, Antonio
dc.contributor.authorChabaa, Samira
dc.contributor.authorZeroual, Abdelouhab
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-02-18T11:21:26Z
dc.date.available2026-02-18T11:21:26Z
dc.date.copyright© 2020 Elsevier Ltd.
dc.date.issued2020-04-21
dc.description.abstractThe accuracy of the prediction of buildings’ energy consumption is being tackled using existing artificial intelligence techniques. However, there is a lack of effort on the development of new techniques for solving that problem and, therefore, achieving higher performance, which is important for the efficient management of energy in many levels. This study addresses this gap by proposing a new hybrid machine learning algorithm that incorporates the adaptive neuro-fuzzy inference system model with a new version of the firefly algorithm denominated as the gender-difference firefly algorithm. We expanded the search space diversification to increase the accuracy on the prediction and adopted the autoregressive process in order to approximate the chaotic behavior of the consumption time series. A new layer, denominated as non-working time adaptation was also integrated so as to decrease the fast variability of the predictions during non-working periods of time. We have applied our algorithm for the consumption prediction on 1 h, 2 h and 3 h ahead horizons. We have obtained improvements on the MAPE and R coefficient when compared with state-of-the-art publications in both a private dataset from the Faculty of Chemistry, located in the city of Murcia, Spain and a public dataset of the consumption of a Retail building located in California, United States. We also show our method’s performance in five more buildings. Our results demonstrate the robustness and the accuracy of our proposal when compared to the traditional adaptive neuro-fuzzy inference system models and also to the different predictive techniques implemented in several pieces of literature.
dc.formatapplication/pdf
dc.format.extent19
dc.identifier.citationApplied Energy, 2020, Vol. 268, 114977
dc.identifier.doihttps://doi.org/10.1016/j.apenergy.2020.114977
dc.identifier.eissn1872-9118
dc.identifier.issn0306-2619
dc.identifier.urihttp://hdl.handle.net/10201/207501
dc.languageeng
dc.publisherElsevier
dc.relationThis work has been sponsored by MINECO through the PERSEIDES project (ref. TIN2017-86885-R) and by the European Commission through the H2020 IoTCrawler (contract 779852), and DEMETER (grant agreement 857202) EU Projects and also co-financed by the European Social Fund (ESF) and the Youth European Initiative (YEI) under the Spanish Seneca Foundation (CARM), and Co-funded by the Erasmus+ programme of the European Union.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S030626192030489X
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectAutoregressive process
dc.subjectGender difference firefly algorithm
dc.subjectBuilding energy consumption
dc.subjectTime series forecasting
dc.subjectANFIS
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleA hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
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relation.isAuthorOfPublication7dacd587-416b-43e7-bec1-30dbb093d0a4
relation.isAuthorOfPublication.latestForDiscoverycf8009bf-6088-449d-9f79-a516af312945
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