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

Abstract

This 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.