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Short-Term Load Interval Prediction Using a Deep Belief Network

Author

Listed:
  • Xiaoyu Zhang

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Zhe Shu

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Rui Wang

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Tao Zhang

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Yabing Zha

    (College of System Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

In load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interval predication is one of the most effective. In this study, a deep belief network-based lower–upper bound estimation (LUBE) approach is proposed, and a genetic algorithm is applied to reinforce the search ability of the LUBE method, instead of simulated an annealing algorithm. The approach is applied to the short-term load prediction on some realistic electricity load data. To demonstrate the effectiveness and efficiency of the proposed method, it is compared with three state-of-the-art methods. Experimental results show that the proposed approach can significantly improve the predication accuracy.

Suggested Citation

  • Xiaoyu Zhang & Zhe Shu & Rui Wang & Tao Zhang & Yabing Zha, 2018. "Short-Term Load Interval Prediction Using a Deep Belief Network," Energies, MDPI, vol. 11(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2744-:d:175383
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    References listed on IDEAS

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    Cited by:

    1. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    2. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
    3. Gangjun Gong & Xiaonan An & Nawaraj Kumar Mahato & Shuyan Sun & Si Chen & Yafeng Wen, 2019. "Research on Short-Term Load Prediction Based on Seq2seq Model," Energies, MDPI, vol. 12(16), pages 1-18, August.

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