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Multi-space collaboration framework based optimal model selection for power load forecasting

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  • Xian, Huafeng
  • Che, Jinxing

Abstract

In recent years, power load forecasting has become a hot and open issue in the field of energy. However, the optimal model selection for power load forecasting is a tricky problem. In this paper, we propose a multi-space collaboration (MSC) framework for optimal model selection. Specifically, our framework adopts space separation strategy to do the model selection on the subspace, which increases the probability of selecting the optimal model; A subspace elimination strategy is introduced, and the subspace with low development potential is gradually eliminated as iteration progresses, making the framework pay more attention to better parameter domain. We conduct a simulation study and a real-world case study of experimental analysis to verify the effectiveness of the proposed framework. On several test functions of known optimal situation, the model selection ability of the MSC framework is better than the ordinary meta-heuristic algorithms, and it has excellent robustness. In addition, the results of the real-world case study show that the optimal SVR model selected by our framework is absolutely superior to various comparison models, and our framework has strong adaptability to the candidate size of the parameter domain.

Suggested Citation

  • Xian, Huafeng & Che, Jinxing, 2022. "Multi-space collaboration framework based optimal model selection for power load forecasting," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003567
    DOI: 10.1016/j.apenergy.2022.118937
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    Cited by:

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    2. Bujin Shi & Xinbo Zhou & Peilin Li & Wenyu Ma & Nan Pan, 2023. "An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection," Energies, MDPI, vol. 16(19), pages 1-20, October.
    3. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    4. Li, Ru & Tang, Bao-Jun & Yu, Biying & Liao, Hua & Zhang, Chen & Wei, Yi-Ming, 2022. "Cost-optimal operation strategy for integrating large scale of renewable energy in China’s power system: From a multi-regional perspective," Applied Energy, Elsevier, vol. 325(C).
    5. Mehmood, Faiza & Ghani, Muhammad Usman & Ghafoor, Hina & Shahzadi, Rehab & Asim, Muhammad Nabeel & Mahmood, Waqar, 2022. "EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting," Applied Energy, Elsevier, vol. 324(C).
    6. Che, Jinxing & Yuan, Fang & Zhu, Suling & Yang, Youlong, 2022. "An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load," Applied Energy, Elsevier, vol. 328(C).
    7. Hany Habbak & Mohamed Mahmoud & Khaled Metwally & Mostafa M. Fouda & Mohamed I. Ibrahem, 2023. "Load Forecasting Techniques and Their Applications in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.

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