电子科技2024，Vol.37Issue(4)：69-76,8.DOI:10.16180/j.cnki.issn1007-7820.2024.04.010
基于k-sums分段聚类的动态组合学习光伏短期功率预测
Short-Term Photovoltaic Power Prediction Based on k-sums Quadratic Clustering and Dynamic Combination Learning
摘要
Abstract
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.关键词
k-sums/分层聚类/TCN/改进GRU/SVM/动态组合学习/Elastic Net/光伏短期功率预测Key words
k-sums/hierarchical clustering/TCN/improve GRU/SVM/dynamic combination learning/Elastic Net/PV short-term power prediction分类
信息技术与安全科学引用本文复制引用
吴家葆,曾国辉,张振华..基于k-sums分段聚类的动态组合学习光伏短期功率预测[J].电子科技,2024,37(4):69-76,8.基金项目
国家自然科学基金(61701296)National Natural Science Foundation of China(61701296) （61701296）
