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A meta-learning based distribution system load forecasting model selection framework

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  • Li, Yiyan
  • Zhang, Si
  • Hu, Rongxing
  • Lu, Ning

Abstract

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.

Suggested Citation

  • Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:appene:v:294:y:2021:i:c:s0306261921004591
    DOI: 10.1016/j.apenergy.2021.116991
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    Cited by:

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    2. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    3. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
    4. Xin Ren & Yimei Wang & Zhi Cao & Fuhao Chen & Yujia Li & Jie Yan, 2023. "Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting," Energies, MDPI, vol. 16(17), pages 1-13, August.

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