综合智慧能源2025，Vol.47Issue(2)：71-78,8.DOI:10.3969/j.issn.2097-0706.2025.02.007
基于K-means聚类的LSTM-SVR-DE光伏功率组合预测
Photovoltaic power prediction based on K-means clustering and the LSTM-SVR-DE model
摘要
Abstract
To improve the accuracy of photovoltaic power prediction,a combined prediction model based on Long Short-Term Memory(LSTM)neural networks and Support Vector Regression(SVR)was proposed.Both the LSTM and SVR models were used separately to predict photovoltaic power.On this basis,a Stacking ensemble strategy was employed to linearly combine the predictions of these two models,with the Differential Evolution(DE)algorithm optimizing the weight coefficients.Simulations and comparative analyses were conducted using real data from a photovoltaic power station in Ningxia.The results showed that the proposed method reduced prediction errors by approximately 70%compared to the LSTM and SVR models.关键词
K-means聚类/LSTM神经网络/支持向量回归/差分进化法/光伏功率预测Key words
K-means clustering/LSTM neural network/support vector regression/differential evolution/photovoltaic power prediction分类
能源科技引用本文复制引用
张元曦,杨国华,杨娜,李祯,马鑫,刘浩睿,南少帅..基于K-means聚类的LSTM-SVR-DE光伏功率组合预测[J].综合智慧能源,2025,47(2):71-78,8.基金项目
宁夏大学研究生创新项目(CXXM2024-01) Ningxia University Graduate Innovation Project(CXXM 2024-01) （CXXM2024-01）
