Research on Ultra-Short-Term Wind Power Prediction Considering Source Relevance

Yong Sun, Zhenyuan Li, Xinnan Yu, Baoju Li, Mao Yang

IEEE Access · 01 January 2020

In one sentence. Ultra-short-term wind power forecasting combines day-ahead NWP-based timing of low-accuracy periods with a neural network and persistence, improving accuracy over classical methods.

Abstract

Wind power forecasting, to a certain extent, will transform the random fluctuation of wind power output into a known situation, which is one of the effective approaches to deal with large-scale wind power integrated into power grid. Due to the use of only historical data and the lack of new information, the accuracy of ultra-short-term wind power prediction (WPP) is still not satisfactory. Therefore, a combined prediction method based on the day-ahead Numerical Weather Prediction (NWP) location technology is proposed. Firstly, the time points with low forecasting accuracy of rolling WPP are approximately located by the NWP information and time windows, and then the hybrid approach combined with neural network and persistence method is presented to predict the future wind power output. The results of the case study show that compared with other classical prediction methods, this method can effectively improve the ultra-short-term prediction accuracy of wind power and verify the effectiveness of the proposed method.

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