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<p>This study presents an optimized machine learning framework for short-term electricity demand forecasting to support energy purchase planning and operational reliability of power distribution systems—particularly under market restructuring and climate variability. The proposed method employed a Random Forest (RF) algorithm enhanced through Grid Search hyperparameter tuning. Temperature data, as a key environmental factor, along with previous-day electricity demand, were used as input features. The model was evaluated using real-world data from a distribution substation in an Iranian city and was compared with conventional forecasting approaches, including Exponential Smoothing (ES), Seasonal ARIMA (SARIMA), and the standard RF. The optimized RF model achieved a Mean Square Error (MSE) of 0.01 and an R² of 0.894 in winter, outperforming the other methods across all seasons. These results confirmed that systematic hyperparameter optimization can significantly enhance the predictive performance of machine learning-based models for day-ahead electricity load forecasting.</p>

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An optimized machine learning approach for short-term electricity demand forecasting considering environmental and historical influencing factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月31日

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  • Hossein Lotfi,
  • Peyman Vafadoost,
  • Hamidreza Rokhsati,
  • Hossein Parsadust

摘要

This study presents an optimized machine learning framework for short-term electricity demand forecasting to support energy purchase planning and operational reliability of power distribution systems—particularly under market restructuring and climate variability. The proposed method employed a Random Forest (RF) algorithm enhanced through Grid Search hyperparameter tuning. Temperature data, as a key environmental factor, along with previous-day electricity demand, were used as input features. The model was evaluated using real-world data from a distribution substation in an Iranian city and was compared with conventional forecasting approaches, including Exponential Smoothing (ES), Seasonal ARIMA (SARIMA), and the standard RF. The optimized RF model achieved a Mean Square Error (MSE) of 0.01 and an R² of 0.894 in winter, outperforming the other methods across all seasons. These results confirmed that systematic hyperparameter optimization can significantly enhance the predictive performance of machine learning-based models for day-ahead electricity load forecasting.