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This is a Quantitative Study study.

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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Analysis reveals impact of economic and oil factors on electricity prices, highlighting machine learning's predictive power.

The Random Forest algorithm achieved superior predictive accuracy, showing promise for accurately estimating electricity prices.
Key influencing factors included exchange rate and Producer Price Index, underscoring their role in pricing dynamics.
Machine learning models were applied to monthly data from 2015 to 2024, reflecting current energy market conditions.
Findings support the use of machine learning in regional energy management and policy-making for better forecasting.

Yasemin Ayaz Atalan (2025) studied this question.

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View Full Paper

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Authors

Analysis reveals impact of economic and oil factors on electricity prices, highlighting machine learning's predictive power.

The Random Forest algorithm achieved superior predictive accuracy, showing promise for accurately estimating electricity prices.
Key influencing factors included exchange rate and Producer Price Index, underscoring their role in pricing dynamics.
Machine learning models were applied to monthly data from 2015 to 2024, reflecting current energy market conditions.
Findings support the use of machine learning in regional energy management and policy-making for better forecasting.

Yasemin Ayaz Atalan (2025) studied this question.

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