Data-driven energy consumption prediction of a university office building using machine learning algorithms

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dc.contributor.authorYesilyurt, Hasan
dc.contributor.authorDokuz, Yesim
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:31:41Z
dc.date.available2024-11-07T13:31:41Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractRedundant consumption of energy in buildings is an important issue that causes increasing problems of climate change and global warming in the world. Therefore, it is necessary to develop efficient energy management approaches in buildings. Accurate prediction of energy consumption plays an important role to obtain energyefficient buildings. Data-driven methods gained attention for estimation of energy consumption in buildings which would provide more accurate prediction results. In this study, hourly energy consumption prediction is performed on a university office building to increase energy efficiency in the building using machine learning algorithms. A new parameter is proposed, air conditioning demand, to improve accuracy of the algorithms. Moreover, temporal parameters, i.e. day of week, month of year, and hour of day, were used along with meteorological parameters to improve prediction performance of the algorithms. Experimental results show that hourly energy consumption of the building could be predicted using machine learning algorithms with high performance. When the results were analysed, Deep Neural Network (DNN) achieved better performance among other alternative algorithms. The average values of R2, RMSE and MAPE for DNN were 0.959, 4.796 kWh, and 5.738 %, respectively. Also, the addition of proposed air conditioning demand parameter provided improved performance to the algorithms.
dc.identifier.doi10.1016/j.energy.2024.133242
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.scopus2-s2.0-85204705682
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.energy.2024.133242
dc.identifier.urihttps://hdl.handle.net/11480/14974
dc.identifier.volume310
dc.identifier.wosWOS:001325000500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEnergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectBuilding energy consumption prediction
dc.subjectMachine learning
dc.subjectDeep learning
dc.subjectData-driven models
dc.subjectEnergy efficiency
dc.subjectSustainable buildings
dc.titleData-driven energy consumption prediction of a university office building using machine learning algorithms
dc.typeArticle

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