Electricity price forecasting using generalized regression neural network based on principal components analysis

被引：1
|
作者：
牛东晓 [1 ]
刘达 [1 ]
邢棉 [2 ]
机构：
[1] School of Business Administration, North China Electric Power University
[2] School of Mathematics and Physics, North China Electric Power
来源：
Journal of Central South University of Technology. | 2008年 / 15(S2)卷 / S2期
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摘要：
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
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页数：5
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