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A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer

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  • Zhang, Dongxue
  • Wang, Shuai
  • Liang, Yuqiu
  • Du, Zhiyuan

Abstract

As the transitions of the power industry to decarburization and distributed energy systems, the future uncertainty information of electric load is becoming essential in power systems planning and operation. However, a great number of studies focus on point forecasting, which only provides the expected value at each time step and it cannot provide uncertainty information. This paper proposed a novel probabilistic load forecasting model by combining quantile regression (QR) with a hybrid model to improve smart grid reliability. In addition, to further improve accuracy and solve the problem that the optimal model is not unique, we propose a new combined probabilistic forecasting model (CPFM). The CPFM employs the traditional statistical models and QR-machine learning models as alternative models; several alternative models with the best performance are combined through the improved multi-objective optimizer to obtain the final forecasting results. The ISO New England data is modeled as a case study to verify the effectiveness of the proposed CPFM. The comparative study includes 13 models, and the results show that the proposed CPFM has better performance in reliability, resolution, and sharpness.

Suggested Citation

  • Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222030584
    DOI: 10.1016/j.energy.2022.126172
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    2. Bianca Magalhães & Pedro Bento & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2024. "Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection," Energies, MDPI, vol. 17(8), pages 1-21, April.
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    6. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    7. Xinghua Wang & Zilv Li & Chenyang Fu & Xixian Liu & Weikang Yang & Xiangyuan Huang & Longfa Yang & Jianhui Wu & Zhuoli Zhao, 2024. "Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula," Sustainability, MDPI, vol. 16(19), pages 1-25, September.

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