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12th Annual Meeting of China Association for Science and Technology on Information and Communication Technology and Smart Grid (AMCST 2010 E-BOOK)

Fuzhou,China,11.1-11.3，2010

ISBN: 978-1-935068-23-5 Scientific Research Publishing, USA

E-Book 660pp Pub. Date: November 2010

Category: Computer Science & Communications

Price: $120

Title: Electric Sort-term Load Forecasting Using Artificial Neural Networks and Fuzzy Expert System
Source: 12th Annual Meeting of China Association for Science and Technology on Information and Communication Technology and Smart Grid (AMCST 2010 E-BOOK) (pp 12-15)
Author(s): He-ru Sun, Power Transmission Technology College, Northeast DianLi University, JiLin JiLin, China
Wei Wang, Economy Management College, Northeast DianLi University, JiLin JiLin, China
Abstract: Use the Radical basis function (RBF) network and Ordinary Least Square (OLS) to determine RBF func- tion centers. The initial load is forecasted by the trained RBF networks, and then, the fuzzy expert systems modify the initial load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. Some of the Day types are divided into five classes in this paper. Test results show that the hybrid model can forecast load with a higher accuracy with a faster speed. Supporting a hybrid model for short-term load forecasting which inte- grates artificial neural networks (ANN) and fuzzy expert system.
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