基于k-sums分段聚类的动态组合学习光伏短期功率预测OA

At present,the prediction accuracy of a single model is difficult to remain optimal with power fluctu-ation.To improve the stability of grid connected system operation and energy saving dispatching of power grid,this study proposes a dynamic learning combination short-term power prediction method based on k-sums hierarchical clustering.The weather types are divided into sunny day A1,cloudy day A2,and rainy day B through segmentation clustering using k-sums algorithm.The TCN(Temporal Convolutional Network)is used to extract the time series characteristics of data,and the GRU(Gate Recurrent Unit)structure of the fusion extraction time series characteris-tics module is established with GRU to achieve the effect of being sensitive to the time series characteristics.After dy-namically combining the improved GRU structure with the SVM(Support Vector Machine),the Elastic Net algorithm is adopted to output the best weight value to obtain the final prediction value.The power data of photovoltaic power generation and corresponding meteorological data of a region in Jiangsu are used to verify the proposed method.The results show that the MAE(Mean Absolute Error)of the dynamic combination learning model is 1.888,and the RMSE(Root Mean Squared Error)is 2.403.