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<p>As the world shifts toward smarter, more sustainable energy systems, accurate power demand forecasting has become a cornerstone of efficient grid management. The steady increase in the density of the energy consumption pattern coupled with the incorporation of renewable energy sources tends to make prior and precise forecasting extremely challenging by their tradition approaches and models. This research proposes an advance hybrid machine learning mode developed by integrating GBM and Bi-LSTM networks to overcome the mentioned challenges. In this study, the GBM is capable of fitting the nonlinear relation effectively and the Bi-LSTM improves the performance by reconstructing the temporal features of the time series data. Based on industrial indices, the performance of the proposed model is analyzed utilizing the forecast accuracy of 98.6%, the peak demand deviation of 3.2%, energy efficiency ratio of 1.12, and load forecasting error of 4.5&#xa0;MW at 24&#xa0;h Horizon. These results confirm accuracy improvements in forecasting and serve to enhance the energy distribution and stability of the electrical grid. The datasets used in the model are verified using Python-based tools including Scikit-learn, XGBoost, and tensor flow to make it efficient and able to cope with real life problems. This kind of innovative hybrid approach closes the gap both for the improvement of forecasting of power demand and for energy optimization, creating the way to more sustainable and resilient energy systems.</p>

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Machine learning-driven power demand forecasting models for optimized power management

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Xiaojun Wang,
  • Linrong Wang,
  • Nan Liang

摘要

As the world shifts toward smarter, more sustainable energy systems, accurate power demand forecasting has become a cornerstone of efficient grid management. The steady increase in the density of the energy consumption pattern coupled with the incorporation of renewable energy sources tends to make prior and precise forecasting extremely challenging by their tradition approaches and models. This research proposes an advance hybrid machine learning mode developed by integrating GBM and Bi-LSTM networks to overcome the mentioned challenges. In this study, the GBM is capable of fitting the nonlinear relation effectively and the Bi-LSTM improves the performance by reconstructing the temporal features of the time series data. Based on industrial indices, the performance of the proposed model is analyzed utilizing the forecast accuracy of 98.6%, the peak demand deviation of 3.2%, energy efficiency ratio of 1.12, and load forecasting error of 4.5 MW at 24 h Horizon. These results confirm accuracy improvements in forecasting and serve to enhance the energy distribution and stability of the electrical grid. The datasets used in the model are verified using Python-based tools including Scikit-learn, XGBoost, and tensor flow to make it efficient and able to cope with real life problems. This kind of innovative hybrid approach closes the gap both for the improvement of forecasting of power demand and for energy optimization, creating the way to more sustainable and resilient energy systems.