Real Time Prediction of Wind Power Based on Relevance Vector Machine

doi:10.11930/j.issn.1004-9649.2016.08.064.05

Abstract

Abstract: The volatility and randomness of wind have significant impacts on the wind power prediction. Accurate and reasonable prediction can ensure a reliable, continuous and stable system operation. An ultra-short term wind power prediction method is proposed based on relevance vector machine. The relevance vector machine is a probability learning model based on the Bayesian theory, and compared with the support vector machine(SVM), it has the advantage of sparse probability model and minor kernel function calculation. Then, based on an analysis of the rolling multi-step prediction model, a short-term wind power prediction model of relevance vector machine is developed. The model has been implemented for wind power prediction to a lot of wind farms in Jilin province, and the testing results show that the proposed model can effectively improve the prediction accuracy and is valuable for engineering implementation.

Key words: wind power, ultra-short term power prediction, relevance vector machine, rolling multi-step prediction

CLC Number:

TM614

YANG Mao, ZHANG Qiang. Real Time Prediction of Wind Power Based on Relevance Vector Machine[J]. Electric Power, 2016, 49(8): 64-68.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.08.064.05

https://www.electricpower.com.cn/EN/Y2016/V49/I8/64

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