摘要
目前单一模型预测精度存在难以随着功率波动保持最优的问题,为提高并网系统运行的稳定性和电网的节能调度,文中提出了一种基于k-sums分层聚类的动态学习组合光伏短期功率预测方法。利用k-sums算法经过分段聚类,将天气类型分为晴天A 1、多云A 2、阴雨天B。通过TCN(Temporal Convolutional Network)提取数据的时序特征,并结合GRU(Gate Recurrent Unit)建立融合提取时序特征模块的改进GRU结构,以达到对时序特征敏感的效果。将改进GRU结构与SVM(Support Vector Machine)动态组合,使用Elastic Net算法输出最佳权重值叠加得到最终预测值。文中采用江苏某地区的光伏发电功率数据及对应的气象数据对所提方法进行验证,结果表明动态组合学习模型的MAE(Mean Absolute Error)为1.888,RMSE(Root Mean Squared Error)为2.403。
At present,the prediction accuracy of a single model is difficult to remain optimal with power fluctuation.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 A_(1),cloudy day A_(2),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 characteristics module is established with GRU to achieve the effect of being sensitive to the time series characteristics.After dynamically 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.
作者
吴家葆
曾国辉
张振华
WU Jiabao;ZENG Guohui;ZHANG Zhenhua(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201600,China)
出处
《电子科技》
2024年第4期69-76,共8页
Electronic Science and Technology
基金
国家自然科学基金(61701296)。
