Consideration of Environmental, Economic, and Oil Factors for Unit-based Estimation of Consumed Electrical Energy with ML Algorithms: A Case Study of Çanakkale Region

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dc.contributor.authorAtalan, Yasemin Ayaz
dc.date.accessioned2026-02-08T15:04:49Z
dc.date.available2026-02-08T15:04:49Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis study focuses on predicting electricity unit prices in the Çanakkale region by analyzing the effects of environmental, economic, and oil-related factors through machine learning (ML) algorithms. The research addresses the accurate prediction of energy costs amid fluctuating market dynamics by applying Random Forest (RF) and k-nearest neighbor (kNN) algorithms to monthly data from 2015 to 2024. The independent variables used in the models include exchange rate (USD/TRY), oil price (TL/liter), Producer Price Index (PPI), Consumer Price Index (CPI), and average temperature. The RF algorithm achieves superior predictive accuracy with an MSE of 0.013, RMSE of 0.112, MAE of 0.081, MAPE of 0.087, and an R² of 0.919, outperforming the kNN model across all metrics. The findings reveal that exchange rate and PPI have the most significant influence on electricity pricing. This study provides empirical evidence supporting the use of ML methods in energy price prediction and contributes to developing more accurate and robust forecasting tools for regional energy management and policy-making.
dc.identifier.doi10.38088/jise.1596664
dc.identifier.endpage258
dc.identifier.issn2602-4217
dc.identifier.issue2
dc.identifier.startpage247
dc.identifier.urihttps://doi.org/10.38088/jise.1596664
dc.identifier.urihttps://hdl.handle.net/20.500.12885/4223
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.subjectEnvironmentally Sustainable Engineering
dc.subjectÇevresel Olarak Sürdürülebilir Mühendislik
dc.titleConsideration of Environmental, Economic, and Oil Factors for Unit-based Estimation of Consumed Electrical Energy with ML Algorithms: A Case Study of Çanakkale Region
dc.typeArticle

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