Short-Term Electric Load Forecasting for an Industrial Plant Using Machine Learning-Based Algorithms

Oğuzhan Timur () and Halil Yaşar Üstünel
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Oğuzhan Timur: Department of Electrical and Electronics Engineering, Çukurova University, Adana 01330, Türkiye
Halil Yaşar Üstünel: Department of Electrical and Electronics Engineering, Çukurova University, Adana 01330, Türkiye

Energies, 2025, vol. 18, issue 5, 1-22

Abstract: As the global energy landscape evolves towards sustainability, the extensive usage of fossil fuels in electricity generation is progressively diminishing, while the contribution of renewable energy sources is steadily increasing. In this evolving scenario, the importance of load forecasting cannot be overstated in optimizing energy management and ensuring the efficient operation of industrial plants regardless of their scale. By accurately anticipating energy demand, industrial facilities can enhance efficiency, reduce costs, and facilitate the adoption of renewable energy technologies in the power grid. Recent studies have emphasized the pervasive utilization of machine learning-based algorithms in the field of electric load forecasting for industrial plants. Their capacity to analyze intricate patterns and enhance prediction accuracy renders them a favored option for enhancing energy management and operational efficiency. The present analysis revolves around the creation of short-term electric load forecasting models for a large industrial plant operating in Adana, Turkey. The integration of calendar, meteorological, and lagging electrical variables, along with machine learning-based algorithms, is employed to boost forecasting accuracy and optimize energy utilization. The ultimate objective of the present study is to conduct a thoroughgoing and detailed analysis of the statistical performance of the models and associated error metrics. The metrics employed include the R 2 and MAPE values.

Keywords: multiple linear regression (MLR); group method of data handling (GMDH); multilayer perceptron neural netwok (MLPNN); gradient boost decision tree (GBDT); gene expression programming (GEP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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