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Multistep interval prediction model with adjustable horizon for uncertain power load forecasting

Author

Listed:
  • Guan, Shouping
  • Xu, Chongyang
  • Guan, Tianyi

Abstract

In order to use the prediction interval (PI) method to forecast multistep uncertain power load, this paper proposes a multistep interval prediction model (MIPM) using neural network-based PIs. An iterative lower and upper bounds estimation (ILUBE) neural network with interval input and interval output is constructed, and a new set of PI performance indices with adjustable prediction horizon for ILUBE is proposed to satisfy the needs of high-quality multistep PIs. A knee selection criteria-based multi-objective particle swarm optimization (KMOPSO) algorithm specifically designed for PI is presented to optimize the ILUEB parameters. The power load forecasting of two actual regions is taken as an example, and the proposed MIPM is applied to carry out multistep interval prediction, respectively. The prediction results and the effect of adjusting prediction horizon demonstrate that the proposed model can realize higher quality PIs, even in the situation of arbitrarily adjusting the prediction horizon.

Suggested Citation

  • Guan, Shouping & Xu, Chongyang & Guan, Tianyi, 2025. "Multistep interval prediction model with adjustable horizon for uncertain power load forecasting," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225034103
    DOI: 10.1016/j.energy.2025.137768
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    References listed on IDEAS

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