基于波动特性挖掘的短期光伏功率预测

综合考虑光伏功率受气象因素影响所呈现出的规律性和波动性,对光伏功率波动类型进行划分与聚类识别提出一种基于波动特性挖掘的短期光伏功率预测方法,。在此基础上,利用数值天气预报和基于互信息熵的相关性分析法提取各类功率波动对应的天气波动特征及其强相关气象因子,建立基于波动特性挖掘的长短期记忆网络组合预测模型,挖掘天气波动与光伏功率波动之间的潜在映射规律。最后,识别出待测日天气波动类型与预测模型之间的匹配关系,利用组合预测模型实现光伏功率预测。通过对中国西北地区某光伏电站的预测分析,验证了所提预测方法的有效性。

SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON FLUCTUATION CHARACTERISTICS MINING

A short-term photovoltaic power forecasting method based on fluctuation characteristics mining is proposed in this paper. Firstly, the classification method and cluster identification method of photovoltaic power fluctuation are presented, considering the regularity and volatility of photovoltaic power affected by meteorological factors. Secondly,the Numerical Weather Prediction and the correlation analysis based on mutual information entropy are used to extract the weather fluctuation characteristics and highly correlated meteorological factors corresponding to various power fluctuations. Thirdly,the combined model of the long-short term memory network is put forward to mine the potential mapping relationship between the weather fluctuation and photovoltaic power fluctuation. Finally,after the types of weather fluctuations on the tested day are identified,its photovoltaic powers are predicted by using the combined method. The results of a photovoltaic power station in Northwest China show that the proposed model is effective.