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

Energies

Lianhui Li²  Chunyang Mu³  Shaohu Ding²  Zheng Wang⁴  Runyang Mo¹  Yongfeng Song¹ 

[1] School of Management, Qingdao Technological University, Qingdao 266520, China;College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, China;;State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaState Grid Ningxia Electric Power Design Co. Ltd., Yinchuan 750001, China;

关键词: load forecasting;    robustness;    combination forecast;    Markov chain;    normal cloud model;    immune algorithm;    particle swarm optimization;   

DOI  :  10.3390/en9010020

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.