Application of Fuzzy Neural Network for Short Term Load Forecasting in Mongolia
The main object of this paper is to present a new approach for loading prediction with high precision using RBF neural network (RBFNN) combined with fuzzy logic. The weather variables are considered by fuzzy logic (FL) module, the output of the FL module is used as an input to the RBFNN module in order to help provide it with enough scenarios to perform the final forecast. The RBFNN is chosen to forecast the load due to its rapid learning and generality. A case study in the region of Mongolia is given later to show that fuzzy logic RBFNN has a much better forecasting accuracy than generally RBFNN. The simulation results demonstrate that the suggested model exhibits very good forecast capabilities.
Jianchang Lu Hongling Han
Department of Economics and Management North China Electric Power University Baoding , Hebei P. R. C School of Management and Administration North China Electric Power University Baoding , Hebei P. R.
国际会议
南宁
英文
2007-07-20（万方平台首次上网日期，不代表论文的发表时间）