An improved WM method based on PSO for electric load forecasting

Authors:
Xueming Yang;Jiangye Yuan;Jinsha Yuan;Huina Mao
Affiliations:
Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China and Department of Civil and Environment Engineering, University of Pittsburgh, ...;Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA;Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

The fuzzy system is an important method for intelligent modelling of electric load forecasting, and how to enhance the learning and data mining ability of fuzzy system is crucial for its practical application and the improvement of the load-forecasting accuracy. In this study, a PSO-based improved Wang-Mendel (WM) method is proposed, which is a new combined modelling method based on fuzzy system and evolutionary algorithm. This method adopts a modified Particle swarm optimization (PSO) algorithm to optimize the fuzzy rule centroid of data covered area and thus obtains complete fuzzy rule set through extrapolating. The electric load-forecasting model based on this proposed method is described, and a case study on short-term load forecast illustrates that this method effectively enhances the forecast accuracy of WM method, has a fast convergence rate, and is independent of the forecasting objects.