Short-term Load Forecasting Method

Based on Wavelet and Reconstructed Phase Space Zunxiong Liu, Zhijun Kuang, Deyun Zhang 1.Dept. of Information and Communication Eng, Xi’an Jiaotong University. Xi’an, Shanxi, China. 2.Dept. of Information Eng, East China Jiaotong University. Nanchang, Jiangxi, China Abstract: This paper proposed wavelet combination method for short-term forecasting, which makes merit of wavelet decomposition and different local approximation algorithm in reconstructed phase space. The load series are non-stationary, observed from a chaotic dynamical system. With the view of wavelet multi-resolution analysis, the original load series are decomposed with A Trous algorithm into a relatively stationary residual and a set of wavelet coefficients, which are taken as independent dynamical subsystems. The subsystems are coped with different local approximation algorithms, with corresponding parameters, producing predicted coefficients. The coefficients are reconstructed with wavelet method to obtain prediction value. Case study demonstrates that the wavelet Combination method has a good performance for less-step predictions, compared with the result from direct method to the original series.