Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education(Northeast Electric Power University), Jilin 132012, China
Numerical weather prediction (NWP) has an important impact on the accuracy of ultra-short-term prediction models for wind power. In order to make full use of the NWP information, considering the spatial correlation of multiple wind farms, an ultra-short-term prediction model of wind power based on multi-location NWP and gated recurrent unit (GRU) is proposed. First, the importance of multi-location NWP information on power generation in wind farms is analyzed through random forest method. The cumulative contribution rate is used to extract the effective information in NWP, and the weighted NWP information and historical power data are used as the input variables in the prediction model. Then, the improved gray wolf optimization algorithm is selected to optimize the parameters of the GRU, and a multi-variate time series prediction model is established for ultra-short-term prediction of power generation in wind farms. Finally, the measured data of a wind farm in China is selected for example analysis, and the effectiveness and feasibility of the proposed method are verified.
This work is supported by National Key R&D Program of China (No. 2018YFB0904200).
|||YANG Mao, BAI Yuying. Ultra-short-term Prediction of Wind Power Based on Multi-location Numerical Weather Prediction and Gated Recurrent Unit[J]. Automation of Electric Power Systems,2021,45(1):177-183. DOI:10.7500/AEPS20200521007|