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<p>The increasing complexity of microgrids in residential and industrial settings demands adaptive demand forecasting methods. This study introduces a novel approach that clusters household consumption data to identify distinct energy profiles, enabling precise modeling. A multilayer perceptron network, optimized using the grey wolf optimizer, is tailored to these profiles, dynamically capturing unique consumption behaviors. Also, a random forest model also predicts future demand profiles based on factors like date, day type, and temperature, ensuring accurate profile assignment for each forecasting period. The proposed model achieves high accuracy, with normalized root-mean-squared error values of 0.04 for Case A and 0.05 for Case B, and mean absolute errors of 0.03–0.05&#xa0;kW, respectively. The total predicted demand closely matches the real values (7.37 kWh vs. 7.34 kWh for Case A and 7.31 kWh vs. 7.56 kWh for Case B). Prediction times are low (1.7428&#xa0;s for Case A, 0.8215&#xa0;s for Case B), and model training takes approximately 54&#xa0;min. Compared to existing methods from state of the art, the proposed solution offers lower errors. The proposed combination of clustering, machine learning, and metaheuristic optimization establishes a strong and efficient framework for microgrid demand forecasting.</p> Graphical Abstract <p></p>

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Cluster-based demand prediction: a hybrid approach with grey wolf optimizer and multilayer perceptron network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Dácil Díaz-Bello,
  • Carlos Vargas-Salgado,
  • Lina Montuori,
  • Manuel Alcázar-Ortega

摘要

The increasing complexity of microgrids in residential and industrial settings demands adaptive demand forecasting methods. This study introduces a novel approach that clusters household consumption data to identify distinct energy profiles, enabling precise modeling. A multilayer perceptron network, optimized using the grey wolf optimizer, is tailored to these profiles, dynamically capturing unique consumption behaviors. Also, a random forest model also predicts future demand profiles based on factors like date, day type, and temperature, ensuring accurate profile assignment for each forecasting period. The proposed model achieves high accuracy, with normalized root-mean-squared error values of 0.04 for Case A and 0.05 for Case B, and mean absolute errors of 0.03–0.05 kW, respectively. The total predicted demand closely matches the real values (7.37 kWh vs. 7.34 kWh for Case A and 7.31 kWh vs. 7.56 kWh for Case B). Prediction times are low (1.7428 s for Case A, 0.8215 s for Case B), and model training takes approximately 54 min. Compared to existing methods from state of the art, the proposed solution offers lower errors. The proposed combination of clustering, machine learning, and metaheuristic optimization establishes a strong and efficient framework for microgrid demand forecasting.

Graphical Abstract