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Diffusion-based inpainting approach for multifunctional short-term load forecasting

Author

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  • Zhang, Luliang
  • Jiang, Zongxi
  • Ji, Tianyao
  • Chen, Ziming

Abstract

Short-Term Load Forecasting is of great significance for the economic and stable operation of the power system. Against the background of the breakthrough in generative artificial intelligence based on the Diffusion model, the research on relevant load forecasting methods of the latter is still relatively limited. Therefore, this paper refers to many related excellent works, analyzes the commonalities between image generation tasks and load forecasting tasks, and proposes the Diffusion-based Inpainting Forecasting Method (DIFM). DIFM supports multi-variable inputs and can achieve functions such as load sequence generation, quantile forecasting and missing data imputation, making it a flexible and multifunctional method. The feasibility and performance of this method are validated across multiple datasets, with experimental results revealing that DIFM reduces the mean absolute percentage error by 24.61 % and 17.91 % respectively in short-term load forecasting and load imputation tasks compared to the optimal benchmark models.

Suggested Citation

  • Zhang, Luliang & Jiang, Zongxi & Ji, Tianyao & Chen, Ziming, 2025. "Diffusion-based inpainting approach for multifunctional short-term load forecasting," Applied Energy, Elsevier, vol. 377(PB).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924018257
    DOI: 10.1016/j.apenergy.2024.124442
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    2. Jiang, Zongxi & Zhang, Luliang & Ji, Tianyao, 2023. "NSDAR: A neural network-based model for similar day screening and electric load forecasting," Applied Energy, Elsevier, vol. 349(C).
    3. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
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    1. Yan, Ke & Liu, Jian & Zhang, Jiazhen & Yang, Fan & Gao, Yuan & Du, Yang, 2025. "Robust photovoltaic forecasting under severe data missingness via multi-domain collaboration and covariate interaction," Applied Energy, Elsevier, vol. 401(PB).
    2. Gao, Yuan & Hu, Sile & Chen, Yahui & Khan, Muhammad Farhan & Cheng, Xiaolei & Yang, Jiaqiang, 2026. "A novel probabilistic wind power forecasting framework integrating similar curve matching mechanism and an enhanced conditional diffusion model," Applied Energy, Elsevier, vol. 402(PB).
    3. Wang, Jun & Zhang, Xuanyu & Wang, Yonggang & Liu, Jiashun & Wang, Han & Lin, Jiali & Xu, Chen & Hua, Shuo, 2025. "A zero-shot load forecasting method for extreme weather integrating causal learning and meta-learning," Energy, Elsevier, vol. 334(C).
    4. Zhao, Pengfei & Shen, Zhirong & Cao, Di & Lin, Zhiping & Chen, Zhe & Hu, Weihao, 2026. "Missing data-aware robust electrical load forecasting based on hierarchical downsampling-upsampling spatiotemporal graph network," Applied Energy, Elsevier, vol. 405(C).

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