Developed Machine Learning Technology and its Application on Electric Power System
There is a general consensus that the movement of electricity price is crucial for electricity market. As a practical tool to estimate the future prices, electricity price forecast is of great importance and use for the operations of market participants. So a hybrid forecast model is proposed in this paper that integrates independent component analysis (ICA) with least squares support vector machines (LS-SVM). First, a novel feature extraction method of price influence factors is proposed based on ICA, which aims at mining the latent source-features by using the higher-ordered statistical characteristics. After that, nonlinear regression modeling of electricity price and its extracted features is accomplished by LS-SVM with more efficient training and forecasting. Finally, Californian market data are employed to test the proposed approach.
H. Zheng et al., "Developed Machine Learning Technology and its Application on Electric Power System", Advanced Materials Research, Vols. 217-218, pp. 1289-1292, 2011