Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital

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dc.authoridIBRIKCI, Turgay/0000-0003-1321-2523
dc.authoridZor, Kasim/0000-0001-6443-114X
dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.authoridteke, ahmet/0000-0003-2610-9576
dc.contributor.authorTimur, Oguzhan
dc.contributor.authorZor, Kasim
dc.contributor.authorCelik, Ozgur
dc.contributor.authorTeke, Ahmet
dc.contributor.authorIbrikci, Turgay
dc.date.accessioned2025-01-06T17:37:35Z
dc.date.available2025-01-06T17:37:35Z
dc.date.issued2020
dc.description.abstractElectrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting.
dc.description.sponsorshipScientific Research Project Unit of Cukurova University [FYL-2014-2351]
dc.description.sponsorshipThis work was supported by the Scientific Research Project Unit of Cukurova University [grant number FYL-2014-2351]. The authors are grateful for the research data provided by Turkish State Meteorological Service and the regional hospital. The authors also would like to thank the anonymous reviewers for their valuable comments and suggestions.
dc.identifier.doi10.13044/j.sdewes.d7.0306
dc.identifier.endpage536
dc.identifier.issn1848-9257
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85081602452
dc.identifier.scopusqualityQ1
dc.identifier.startpage520
dc.identifier.urihttps://doi.org/10.13044/j.sdewes.d7.0306
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2291
dc.identifier.volume8
dc.identifier.wosWOS:000537533200008
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInt Centre Sustainable Dev Energy Water & Env Systems-Sdewes
dc.relation.ispartofJournal of Sustainable Development of Energy Water and Environment Systems-Jsdewes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectElectrical energy forecasting
dc.subjectMedium-term
dc.subjectMultiple linear regression
dc.subjectArtificial neural networks
dc.subjectSupport vector machines
dc.subjectHospital
dc.titleApplication of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital
dc.typeArticle

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