基于改进霜冰优化算法的短期光伏功率预测方法研究
Research on Short-Term Photovoltaic Power Prediction Method Based on an Improved Rime Optimization Algorithm
摘要
Abstract
[Objectives]Photovoltaic power generation is characterized by randomness,intermittency,and volatility.Its large-scale grid integration may impact power systems,thereby posing serious challenges to grid operation and scheduling.Therefore,developing a high-precision photovoltaic power prediction model and accurately characterizing the fluctuation patterns of photovoltaic power under varying meteorological conditions are of great significance for ensuring the stable operation of the power grid.To this end,this study proposes a photovoltaic power prediction method based on an intelligent optimization algorithm and a combined prediction model.[Methods]First,a residual network(ResNet)-LSTM-Dropout combined photovoltaic power prediction model is constructed with the long short-term memory network(LSTM)as the core model.In this model,the ResNet is used to extract deep nonlinear features from complex meteorological data,LSTM is employed to learn the temporal variation patterns of photovoltaic power sequences,and the Dropout layer is introduced to reduce the risk of overfitting during model training on complex samples,thereby improving the generalization performance of the model.Second,to address the problems that the rime optimization algorithm(RIME)easily falls into local optima and has slow convergence speed,the cosine strategy is introduced to improve local exploration capability,the role strategy is adopted to enhance global search capability,and the Cauchy mutation strategy is incorporated to avoid premature convergence.Accordingly,an improved rime optimization algorithm(IRIME)is proposed.Finally,IRIME is used to optimize the key hyperparameters of the ResNet-LSTM-Dropout combined model,and the optimized model is applied to short-term photovoltaic power prediction.[Results]The effectiveness of the proposed model is verified using measured data from a photovoltaic power station in Northwest China.The experimental results show that,under different weather conditions,the IRIME-ResNet-LSTM-Dropout model outperforms other comparison models in prediction performance,with more significant advantages under complex weather conditions such as cloudy and rainy days.[Conclusions]The proposed method effectively improves the accuracy of photovoltaic power prediction,providing important theoretical support for ensuring safe and stable grid operation and optimizing coordinated planning in power systems.关键词
光伏发电/预测模型/霜冰优化算法(RIME)/长短期记忆网络(LSTM)/残差网络/余弦策略/角色策略/柯西变异策略Key words
photovoltaic power generation/prediction model/rime optimization algorithm(RIME)/long short-term memory network(LSTM)/residual network/cosine strategy/role strategy/Cauchy mutation strategy分类
能源科技引用本文复制引用
王玲芝,赵佳蕊,李万军,李洁,张雄,吕井波..基于改进霜冰优化算法的短期光伏功率预测方法研究[J].发电技术,2026,47(4):761-773,13.基金项目
国家自然科学基金项目(52177194) （52177194）
西安航空职业技术学院配电台区末端源网荷储互动技术创新团队项目(KJTD21-002) （KJTD21-002）
西安航空职业技术学院校级课题(23XHZK-06) （23XHZK-06）
陕西省自然科学基金项目(2025JC-YBMS-482) （2025JC-YBMS-482）
西安邮电大学2025年研究生创新基金项目(CXJJYL2025045). Project Supported by National Natural Science Foundation of China(52177194) （CXJJYL2025045）
Innovation Team of Terminal Source-Grid-Load-Storage Interactive Technology in Distribution Area of Xi'an Aeronautical Polytechnic Institute(KJTD21-002) （KJTD21-002）
Xi'an Aeronautical Polytechnic Institute University-Level Project(23XHZK-06) （23XHZK-06）
Program of Shaanxi Provincial Natural Science Foundation(2025JC-YBMS-482) （2025JC-YBMS-482）
Graduate Student Innovation Fund Project of Xi'an University of Posts and Telecommunications in 2025(CXJJYL2025045). （CXJJYL2025045）
