Short-Term Load Forecasting Based on Fuzzy Clustering Wavelet Decomposition and BP Neural Network
Power and Energy Engineering Conference（2011）
摘要
This paper proposes a composite method for short-term load forecasting, which is based on fuzzy clustering wavelet decomposition and BP neural network. Firstly, the similar-day's load is selected as the input load based on the fuzzy clustering method; secondly, the wavelet method is applied to decompose the similar-day load into the low frequency and high frequency components, from which the feature of each load component can be captured. Finally, the separate neural network model is used to predict each load component, and the value of the forecasted load is obtained by superimposing the prediction value of each load component. The method proposed in this paper is tested on an actual power load in the year of 2010, and the results are compared with two other existing methods, which show that this method provides more accurate predictions.
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关键词
bp neural network,fuzzy set theory,pattern clustering,wavelet method,load component,wavelet transforms,fuzzy clustering wavelet decomposition,short-term load forecasting,backpropagation,power engineering computing,load forecasting,neural nets,low frequency,high frequency,neural network,indexation,indexes,artificial neural networks,fuzzy clustering,neural network model,wavelet transform,accuracy,artificial neural network
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