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dc.contributor.authorAdegoke, Muideen
dc.contributor.authorHafiz, Alaka
dc.contributor.authorAjayi, Saheed
dc.contributor.authorOlu-Ajayi, Razak
dc.contributor.editorNastasi, Benedetto
dc.contributor.editorSantos, Paulo
dc.date.accessioned2023-01-04T11:15:02Z
dc.date.available2023-01-04T11:15:02Z
dc.date.issued2022-12
dc.identifier.citationAdegoke , M , Hafiz , A , Ajayi , S , Olu-Ajayi , R , Nastasi , B (ed.) & Santos , P (ed.) 2022 , ' Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction ' , Energies , vol. 15 , no. 24 , 9512 , pp. 1-21 . https://doi.org/10.3390/en15249512
dc.identifier.issn1996-1073
dc.identifier.otherJisc: 822676
dc.identifier.otherpublisher-id: energies-15-09512
dc.identifier.otherORCID: /0000-0003-0326-1719/work/159834888
dc.identifier.urihttp://hdl.handle.net/2299/25975
dc.description© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.description.abstractBuilding energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction.en
dc.format.extent21
dc.format.extent649403
dc.language.isoeng
dc.relation.ispartofEnergies
dc.subjectArticle
dc.subjectenergy prediction
dc.subjectbuilding energy consumption
dc.subjectmachine learning
dc.subjectenergy efficiency
dc.subjectControl and Optimization
dc.subjectEnergy (miscellaneous)
dc.subjectEngineering (miscellaneous)
dc.subjectEnergy Engineering and Power Technology
dc.subjectElectrical and Electronic Engineering
dc.subjectBuilding and Construction
dc.subjectFuel Technology
dc.subjectRenewable Energy, Sustainability and the Environment
dc.titleApplication of Multilayer Extreme Learning Machine for Efficient Building Energy Predictionen
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionUniversity of Hertfordshire
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionCentre for Climate Change Research (C3R)
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85144606053&partnerID=8YFLogxK
rioxxterms.versionofrecord10.3390/en15249512
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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