基于集群划分的光伏电站集群发电功率短期预测方法

Short-term Power Output Forecasting of Clustered Photovoltaic Solar Plants Based on Cluster Partition

光伏发电集群的功率预测对区域光伏发电的优化调度意义重大。为提升光伏电站集群功率预测精度,提出了基于K均值聚类划分的光伏集群短期功率预测方法,以场站光伏发电特征为参照,进行集群聚类划分,并引入带补偿偏置的长短期记忆网络(bias compensation long short-term memory network,BC-LSTM)进行功率预测。算例结果表明,使用带补偿偏置的长短期记忆网络相较于长短期记忆网络网络(long short-term memory network,LSTM)能够提升约0.6%的预测精度,使用集群累加法相较于统计升尺度法和累加法也能够提升约0.5%的预测精度。

The power prediction of photovoltaic power generation cluster is of great significance to the optimal dispatch of regional photovoltaic power generation.The research on photovoltaic cluster power prediction content is rarely available in the existing literature.In order to improve the power prediction accuracy of photovoltaic power station clusters,a short-term power prediction method for photovoltaic clusters based on K-means clustering is proposed.Based on the characteristics of photovoltaic power generation at the station,the clustering kind is divided,and the bias-compensation long-short-term memory(BC-LSTM)network is used for power prediction.The results show that the BC-LSTM can improve the prediction accuracy by about 0.6%compared with long short-term memory network(LSTM)network,and the cluster accumulation method can also be adopted to improve about 0.5%compared with the statistical upscaling method and the accumulation method.