Application of Independent Component Analysis in Short-Term Power Forecasting of Wind Farm

Abstract:

For the difficult problems of measuring and forecasting values interfered by a number of factors, this paper proposed a method of power forecasting based on independent component analysis and least squares support vector machine, and results are modified using the regression. Each independent component from source signals is predicted using least squares support vector machine, the final prediction results obtained by modifying the preliminary predicting power according to the relationship between wind speed and its power. Using the data from a wind farm on the Northeast China wind farm, the simulation results show that this method has higher prediction accuracy, and the mean absolute error from 9.25% down to 5.48%, compared with the simple least squares support vector machine models.

• Periodical: Applied Mechanics and Materials (Volumes 63-64)
• Pages: 124-128
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.63-64.124
• Online since: June 2011
• Authors: Guo Chu Chen, Peng Wang, Jin Shou Yu
• Keywords: Forecasting, Independent Component Analysis (ICA), Least Squares Support Vector Machines (Ls-SVM), Nonlinear Regression, Wind Power

References

[1] YANG Xiu-yuan, XIAO Yang and CHEN Shu-yong: Wind Speed and Generated Power Forecasting in Wind Farm, Proceedings of the CSEE, Vol. 25, No. 11 (2005), pp.1-5.

[2] Ma Lei, Luan Shi-yan, Jiang Chuan-wen and Liu Hong-ling: A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, Vol. 13 (2009), pp.915-920.

DOI: 10.1016/j.rser.2008.02.002

[3] FAN Gao-feng, WANG Wei-sheng, LIU Chun and DAI Hui-zhu: Wind Power Prediction Based on Artificial Neural Network, Proceedings of the CSEE, Vol. 28, No. 34 (2008), pp.118-123.

[4] P. Louka, G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, I. Pytharoulis and G. Kallos: Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Wind Engineering and Industrial Aerodynamics, Vol. 96 (2008).

DOI: 10.1016/j.jweia.2008.03.013

[5] Mehmet Bilgili, Besir Sahin and Abdulkadir Yasar: Application of artificial neural networks for the wind speed prediction of target station using reference stations data, Renewable Energy, Vol. 32 (2007), p.2350–2360.

DOI: 10.1016/j.renene.2006.12.001

[6] Jin-Cherng Lin, Yung-Hsin Li and Cheng-Hsiung Liu: Building Time Series Forecasting Model by Independent Component Analysis Mechanism, Proceedings of the World Congress on Engineering, No. 2 (2007).

[7] XIE Hong, LIU Min and CHEN Shu-rong: Forecasting model of short-term traffic flow for road network based on independent component analysis and support vector machine, Journal of Computer Application, Vol. 29, No. 9 (2009), pp.2550-2553.

DOI: 10.3724/sp.j.1087.2009.02550

[8] ZHOU Zong-tan, DONG Guo-hua, XU Xin and HU De-wen: Independent Component Analysis, Publishing House of Electronics Industry (2007).

[9] XIAO Peng, DU Yan-fei: Research on Financial Distress Prediction Based on Independent Component Analysis and Fuzzy Support Vector Machine, JOURNAL OF SHAANXI UNIVERSITY OF SCIENCE & TECHNOLOGY, Vol. 27, No. 4 (2009), pp.151-154.