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An improved WM method based on PSO for electric load forecasting

Authors
Journal
Expert Systems with Applications
0957-4174
Publisher
Elsevier
Publication Date
Volume
37
Issue
12
Identifiers
DOI: 10.1016/j.eswa.2010.05.085
Keywords
  • Particle Swarm Optimization (Pso)
  • Electric Load Forecasting
  • Fuzzy Systems
  • Wang–Mendel (Wm) Method
Disciplines
  • Computer Science

Abstract

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.

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