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A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Author

Listed:
  • Lianhui Li

    (College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, China
    These authors contributed equally to this work.)

  • Chunyang Mu

    (State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China
    These authors contributed equally to this work.)

  • Shaohu Ding

    (College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, China)

  • Zheng Wang

    (State Grid Ningxia Electric Power Design Co. Ltd., Yinchuan 750001, China)

  • Runyang Mo

    (School of Management, Qingdao Technological University, Qingdao 266520, China
    College of Electrical &Information Engineering, Hunan University, Changsha 410082, China)

  • Yongfeng Song

    (School of Management, Qingdao Technological University, Qingdao 266520, China
    College of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China)

Abstract

Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

Suggested Citation

  • Lianhui Li & Chunyang Mu & Shaohu Ding & Zheng Wang & Runyang Mo & Yongfeng Song, 2015. "A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination," Energies, MDPI, vol. 9(1), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:9:y:2015:i:1:p:20-:d:61556
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    References listed on IDEAS

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