Short-term load forecasting based on wavelet decomposition,fuzzy gray correlation clustering and BP neural network
A short-term load forecasting method is proposed based on the wavelet decomposition,fuzzy gray correlation clustering and BP neural network. The wavelet decomposition is applied to decompose the load series into the low-frequency and high-frequency components to find the law of each load component. The fuzzy gray correlation clustering is applied to select the days with the load similar to the day to be forecasted. Corresponding neural network model is used to forecast the load for each component. The forecasted load is the superposition of all forecasted component loads. The proposed method is applied to forecast the load of an actual region for three days in 2010 and the results are compared with those by other existing forecasting methods, which shows that the forecasting accuracy is increased.
NETL
NSTL
万方数据
维普
