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Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

Author

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  • Dongxiao Niu
  • Yan Lu
  • Xiaomin Xu
  • Bingjie Li

Abstract

In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.

Suggested Citation

  • Dongxiao Niu & Yan Lu & Xiaomin Xu & Bingjie Li, 2015. "Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, January.
    • Handle: RePEc:hin:jnlmpe:231765
    DOI: 10.1155/2015/231765
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