Using an adaptive self-tuning approach to forecast power loads

Authors:
Zhiling Lin;Dapeng Zhang;Liqun Gao;Zhi Kong
Affiliations:
School of Electrical Engineering, Tianjin University of Technology, Ttianjin 300191, China;School of Electrical Engineer and Automation, Tianjin University, Tianjin 300072, China;College of Information Science and Engineering, Northeastern University, Shenyang 110004, China;College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
Venue:
Neurocomputing
Year:
2008

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Abstract

To overcome the difficulty in forecasting power loads precisely, an adaptive self-tuning approach is proposed. Using an RBF neural network as a predictor and building an evaluator to evaluate the output of the predictor, then, according to the evaluator's judgment, the predictor adjusts its structure and weight by using critical self-learning mechanics. The predictor can keep the same pattern as the current power loads state and power loads can be forecasted precisely. The simulation of practical date indicated that this method is effective.