Abstract
With the increasing proportion of renewable energy being integrated into the power system, the stable operation of the power system is facing great challenges. Breaking through the limitation and prolonging the time period of power prediction will help to improve the consumption ability of wind power (WP), adjust the power generation plan, and ensure the safe operation of the power system. Based on the availability of long-foresight-term numerical weather prediction (NWP) and fluctuation trend mining, a wind farm cluster power forecasting method based on dynamic perception of fluctuation trend is proposed for the long-foresight-term wind power prediction (WPP). The method first matches historical power based on the fluctuation trend of NWP wind speed (WS), and then obtains statistical characteristics of similar processes in order to construct new input features. Secondly, after the prediction results are obtained, the predicted power is corrected according to the fluctuation trend to improve the prediction reliability. This method is applied to a wind farm cluster in Gansu Province, China, and the overall prediction accuracy is 2.23 % higher than that of the direct prediction method. It is verified that this method can achieve long-foresight-term prediction and increase the reliability of prediction.
Published Version
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