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

Lianhui Li, Chunyang Mu, Shaohu Ding, Zheng Wang, Runyang Mo and Yongfeng Song
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Lianhui Li: College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, China
Chunyang Mu: State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China
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
Yongfeng Song: School of Management, Qingdao Technological University, Qingdao 266520, China

Energies, 2015, vol. 9, issue 1, 1-22

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.

Keywords: load forecasting; robustness; combination forecast; Markov chain; normal cloud model; immune algorithm; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
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