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.
Highlights
Nowadays, the strong smart grid (SSG) is vigorously being constructed and the renewable distributed electricity generation capacity is steadily increasing
We propose a robust weighted combination forecasting method based on forecast model filtering and adaptive variable weight determination
We have proposed a robust weighted combination load forecasting method
Summary
Lianhui Li 1,† , Chunyang Mu 2, *,† , Shaohu Ding 1, *, Zheng Wang 3 , Runyang Mo 4,5 and Yongfeng Song 4,6. Received: 12 August 2015; Accepted: 19 October 2015; Published: 31 December 2015. State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. College of Electrical &Information Engineering, Hunan University, Changsha 410082, China. College of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China.
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