Short term load forecasting using neural network with rough set

  • Authors:

  • Zhi Xiao;Shi-Jie Ye;Bo Zhong;Cai-Xin Sun

  • Affiliations:

  • College of Economics and Business Administration, Chongqing University, Chongqing, China;College of Economics and Business Administration, Chongqing University, Chongqing, China;College of Mathematics and Physics, Chongqing University, Chongqing, China;Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Chongqing University, Chongqing, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison.