Short-Term Load Forecasting Based on the Method of Genetic Programming
Limin Huo, Xinqiao Fan, Yunfang Xie, Jinliang Yin · 2007
The algorithm of genetic programming is described and applied to short-term load forecasting. For the fault in history load data, the load samples are filtered and processed generally before using, and then the load series of the same time point but different days are chosen as the training sets. According to the complex expressive capacity of genetic programming, the future short-term load model of different time point is forecasted by time-sharing. This method of genetic programming can find out relevant elements to electric load data automatically, so the artificial errors in forecasting can be avoided effectively. And the future load value of each time point can be calculated with the corresponding model created. Finally, it proves that the method of genetic programming in short-term load forecasting is better through out comparison between the results forecasted by genetic programming and time series.