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Energies 2016, 9(1), 20; doi:10.3390/en9010020

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

1
College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, China
2
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China
3
State Grid Ningxia Electric Power Design Co. Ltd., Yinchuan 750001, China
4
School of Management, Qingdao Technological University, Qingdao 266520, China
5
College of Electrical &Information Engineering, Hunan University, Changsha 410082, China
6
College of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Paul Flikkema
Received: 12 August 2015 / Revised: 21 September 2015 / Accepted: 19 October 2015 / Published: 31 December 2015
View Full-Text   |   Download PDF [1765 KB, uploaded 31 December 2015]   |  

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. View Full-Text
Keywords: load forecasting; robustness; combination forecast; Markov chain; normal cloud model; immune algorithm; particle swarm optimization load forecasting; robustness; combination forecast; Markov chain; normal cloud model; immune algorithm; particle swarm optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, L.; Mu, C.; Ding, S.; Wang, Z.; Mo, R.; Song, Y. A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination. Energies 2016, 9, 20.

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