Comparative Analysis of Electricity Consumption Forecast

Basit Öğe Kaydı
dc.contributor.authorArslan, Mehmet Ali
dc.contributor.authorTalan, Tarık
dc.date.accessioned2026-02-08T15:04:47Z
dc.date.available2026-02-08T15:04:47Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis study aims to make a comparative analysis of electricity consumption forecast using artificial intelligence (AI) and statistical models. In order to reduce the current deficits of countries, it is of great importance to predict the future electricity consumption amount and plan the power plant capacities accordingly. Electricity is an energy source that is extremely difficult to store when used in sectors such as industry and housing. Therefore, the electricity produced must be consumed immediately without causing energy losses and waste. In this context, ensuring the balance between electricity production and consumption can correctly contribute to the management of the current deficit by increasing economic efficiency. In the current study, Türkiye's hourly electricity consumption data between 2016 and 2024 were examined. These data were transformed into a 108-month consumption data set. Seven different models, namely Auto-ARIMA, Holt-Winters, Theta, ETS, TBATS, NNETAR and MLP, were used in the analyses. Among the models, NNETAR and MLP are AI based, and the others are statistical-based models. In this way, the effectiveness of different model types in electricity consumption estimations was compared. In this study, the Auto-ARIMA model stood out with a 3.77% MAPE error rate. When such studies are considered within the framework of countries' energy policies, they can make a significant contribution to reducing the current deficit of the country's economy. As a result of the study, it was concluded that the Auto-ARIMA model should be taken into consideration when making estimates on how many Megawatt power plants should be built in order to meet future energy needs in shaping energy policies in Türkiye.
dc.identifier.doi10.38088/jise.1619782
dc.identifier.endpage102
dc.identifier.issn2602-4217
dc.identifier.issue1
dc.identifier.startpage89
dc.identifier.urihttps://doi.org/10.38088/jise.1619782
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4170
dc.identifier.volume9
dc.language.isoen
dc.publisherBursa Teknik Üniversitesi
dc.relation.ispartofJournal of Innovative Science and Engineering
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20260207
dc.subjectArtificial Intelligence (Other)
dc.subjectYapay Zeka (Diğer)
dc.titleComparative Analysis of Electricity Consumption Forecast
dc.typeArticle

Dosyalar

Koleksiyon

Journal of Innovative Science and Engineering Koleksiyonu