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Mathematical Problems in Engineering
Volume 2014, Article ID 381387, 9 pages
Research Article

Short-Term Power Generation Energy Forecasting Model for Small Hydropower Stations Using GA-SVM

1Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
2Yunnan Power Dispatching Control Center, Kunming 650011, China

Received 12 May 2014; Revised 26 June 2014; Accepted 27 June 2014; Published 17 July 2014

Academic Editor: Bin Yu

Copyright © 2014 Gang Li 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.


Accurate and reliable power generation energy forecasting of small hydropower (SHP) is essential for hydropower management and scheduling. Due to nonperson supervision for a long time, there are not enough historical power generation records, so the forecasting model is difficult to be developed. In this paper, the support vector machine (SVM) is chosen as a method for short-term power generation energy prediction because it shows many unique advantages in solving small sample, nonlinear, and high dimensional pattern recognition. In order to identify appropriate parameters of the SVM prediction model, the genetic algorithm (GA) is performed. The GA-SVM prediction model is tested using the short-term observations of power generation energy in the Yunlong County and Maguan County in Yunnan province. Through the comparison of its performance with those of the ARMA model, it is demonstrated that GA-SVM model is a very potential candidate for the prediction of short-term power generation energy of SHP.