Ultra-Short-Term Wind Power Forecasting Based on QPSO-LSSVM

doi:10.11930/j.issn.1004-9649.2016.03.183.05

Abstract

Abstract: An accurate forecast of wind power is beneficial to the economic and security dispatch of the power grid. To improve the accuracy of ultra-short-term wind power forecasting, this paper applies the quantum particle swarm optimization(QPSO) to optimize the parameters affecting the regression performance of the least squares support vector machine (LSSVM) in the QPSO-LSSVM model. Based on actual application in a wind farm, it is shown that the QPSO-LSSVM forecasting model has smaller errors on a variety of indicators than the BP neural network based model and PSO-LSSVM model. It has high forecasting accuracy and is robustness, and can be an effective ultra-short-term wind power forecasting methodology.

Key words: wind power forecast, quantum particle swarm, least squares support vector machine (LSSVM), BP neural network

CLC Number:

TM614

ZHANG Tao, SUN Xiaowei, SHI Suyi, LI Zhenxing. Ultra-Short-Term Wind Power Forecasting Based on QPSO-LSSVM[J]. Electric Power, 2016, 49(3): 183-187.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.03.183.05

https://www.electricpower.com.cn/EN/Y2016/V49/I3/183

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