Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

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dc.authoridteke, ahmet/0000-0003-2610-9576
dc.authoridTimur, Oguzhan/0000-0002-6537-7840
dc.authoridZor, Kasim/0000-0001-6443-114X
dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.contributor.authorZor, Kasim
dc.contributor.authorCelik, Ozgur
dc.contributor.authorTimur, Oguzhan
dc.contributor.authorTeke, Ahmet
dc.date.accessioned2025-01-06T17:36:45Z
dc.date.available2025-01-06T17:36:45Z
dc.date.issued2020
dc.description.abstractOver the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today's popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.
dc.description.sponsorshipScientific Project Unit of Cukurova University [FBA-2017-8252, FBA-2017-9344]; Scientific Project Unit of Adana Alparslan Turkes Science and Technology University [19103012]
dc.description.sponsorshipThis research was funded by [the Scientific Project Unit of Cukurova University] grant numbers [FBA-2017-8252] and [FBA-2017-9344], and by [the Scientific Project Unit of Adana Alparslan Turkes Science and Technology University] grant number [19103012].
dc.identifier.doi10.3390/en13051102
dc.identifier.issn1996-1073
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85081624709
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/en13051102
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1994
dc.identifier.volume13
dc.identifier.wosWOS:000524318700089
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofEnergies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectbuilding
dc.subjectelectrical energy consumption
dc.subjectshort-term forecasting
dc.subjectgene expression programming (GEP)
dc.subjectgroup method of data handling (GMDH) networks
dc.titleShort-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks
dc.typeArticle

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