TY - JOUR A2 - Yu, Bin AU - Li, Gang AU - Sun, Yongjun AU - He, Yong AU - Li, Xiufeng AU - Tu, Qiyu PY - 2014 DA - 2014/07/17 TI - Short-Term Power Generation Energy Forecasting Model for Small Hydropower Stations Using GA-SVM SP - 381387 VL - 2014 AB - 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. SN - 1024-123X UR - https://doi.org/10.1155/2014/381387 DO - 10.1155/2014/381387 JF - Mathematical Problems in Engineering PB - Hindawi Publishing Corporation KW - ER -