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Mathematical Problems in Engineering
Volume 2015, Article ID 231765, 8 pages
http://dx.doi.org/10.1155/2015/231765
Research Article

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

School of Economics and Management, North China Electric Power University, Beijing 102206, China

Received 21 October 2014; Revised 9 December 2014; Accepted 16 December 2014

Academic Editor: Dan Simon

Copyright © 2015 Dongxiao Niu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.