Developed Machine Learning Technology and its Application on Electric Power System

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 217-218)

Edited by:

Zhou Mark

Pages:

1289-1292

DOI:

10.4028/www.scientific.net/AMR.217-218.1289

Citation:

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

Online since:

March 2011

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Price:

$35.00

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