Developed Machine Learning Technology and its Application on Electric Power System

Hua Zheng; Li Xie; Li Zi Zhang

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

Paper Titles

Effect of Repeated Induction Heating on Fatigue Crack Propagation in SAE 52100 Bearing Steel
p.1266

Effect of Finishing on the Structure and Filtration Property of PPS Filter
p.1272

Temperature Effect on Microstructure and Mechanical Behavior of Ti-15V-3Cr-3Al-3Sn
p.1277

The Effect of Notch Processing on Fracture of High-Carbon Steel (C70S6)
p.1283

Developed Machine Learning Technology and its Application on Electric Power System
p.1289

Intelligent Simulation Method and its Application on Risk Analysis
p.1293

Localization of Partial Magnetization around Artificial Slits in Square Bars of Medium Carbon Low Alloy Steel JIS S45C
p.1297

Structural Strength Evolution of Soil under Dynamic Load
p.1303

The Higher Order Asymptotic Fields for Anti-Plane Oblique Crack with Physical Weak-Discontinuity
p.1309

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 217-218Developed Machine Learning Technology and its...

Developed Machine Learning Technology and its Application on Electric Power System

Abstract:

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.

You might also be interested in these eBooks

High Performance Structures and Materials Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 217-218)

Pages:

1289-1292

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.217-218.1289

Citation:

Cite this paper

Online since:

March 2011

Authors:

Hua Zheng, Li Xie, Li Zi Zhang

Keywords:

Forecasting, Independent Component Analysis (ICA), Least Squares Support Vector Machines (Ls-SVM), Machine Learning (ML)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Javier Contreras, Rosario Espínola, Francisco J. Ngales, and Antonio J. Conejo: submitted to IEEE Trans. on Power Syst. (2003).

Google Scholar

[2] Antonio J. Conejo, Miguel A. Plazas, Rosa Espínola, and Ana B. Molina: submitted to IEEE Trans. on Power Syst. (2005).

Google Scholar

[3] Alicia Mateo González, Antonio Muñoz San Roque, and Javier García-González: submitted to IEEE Trans. on Power Syst. (2005).

Google Scholar

[4] Y. -Y. Hong, and C. -Y Hsiao: submitted to IEE Proceedings- Gener. Transm. Distrib. (2002).

Google Scholar

[5] B.R. Szkuta, L.A. Sanabria, and T.S. Dillon: submitted to IEEE Trans. on Power Syst. (1999).

Google Scholar

[6] Li Yuan-cheng, Fang Ting-jian, and Yu Er-keng: submitted to Proc. of the CSEE (2003).

Google Scholar

[7] Marcelo Espinoza, Johan A. K Suykens, and Bart De Moor: submitted to Proc. of IEEE Conf. on Decision and Control(2003).

Google Scholar