An adaptive time-resolution method for ultra-short-term wind power prediction

Abstract Accurate wind power prediction (WPP) plays an important role in the secure operation and dispatch of power systems. This paper proposes an adaptive time-resolution method to improve the accuracy of ultra-short-term wind power prediction (USTWPP). Firstly, the hidden prediction error (HPE) with its fluctuation magnitude and rate indicators is defined to reveal the fluctuation characteristics of real-time wind power. Then, the adjustment time of time-resolution can be dynamically determined by evaluating the fluctuation magnitudes, and the adjustment rules are formulated by mining the regularities of fluctuation rate of historical wind power data and establishing the interval grouping optimization model. Finally, by coupling the adjustment time and rules into the prediction model of back propagation neural network (BPNN), the rolling prediction with adaptive adjustment of time-resolution is achieved. Extensive tests have not only demonstrated the validity of the proposed method, but also confirmed its capability to cope with the USTWPP especially under situations of extremely violent fluctuations of wind power.

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