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Use of High-performance Graphics Processing Units for Power System Demand Forecasting

  • He, Ting (School of ITEE, The University of Queensland) ;
  • Meng, Ke (Department of Electrical Engineering, The Hong Kong Polytechnic University) ;
  • Dong, Zhao-Yang (Department of Electrical Engineering, The Hong Kong Polytechnic University) ;
  • Oh, Yong-Taek (Korea University of Technology and Education) ;
  • Xu, Yan (School of ITEE, The University of Queensland)
  • Received : 2010.01.18
  • Accepted : 2010.05.07
  • Published : 2010.09.01

Abstract

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

Keywords

Artificial Neural Network;Graphics Processing Unit;Multi-layer Perceptron;Short-term Load Forecasting

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