湖南大学学报（自然科学版）2025，Vol.52Issue(12)：100-112,13.DOI:10.16339/j.cnki.hdxbzkb.2025230
基于EGO-CEEMDAN-VMD-BiGRU模型的短期光伏发电功率预测方法
Short-term Photovoltaic Power Prediction Method Based on the EGO-CEEMDAN-VMD-BiGRU Model
摘要
Abstract
To improve the prediction accuracy of photovoltaic power,an EGO-CEEMDAN-VMD-BiGRU short-term photovoltaic power prediction model is proposed based on the dual data decomposition method of parameter optimization.Initially,the eel and grouper optimizer(EGO)algorithm is employed to determine the optimal parameters for complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),thereby performing an initial decomposition of the photovoltaic dataset.Subsequently,the K-means clustering algorithm is utilized to categorize the modal components into high-frequency,medium-frequency,and low-frequency groups,effectively reducing redundancy among the components.Then,the EGO algorithm is used to optimize the parameters of variational mode decomposition(VMD).Following this,the high-frequency component is decomposed for the second time to mitigate the non-stationarity of the sequence.Finally,bidirectional gated recurrent unit(BiGRU)is applied to predict the components derived from the two-stage decomposition process,with the final prediction result obtained through summation.Based on the dataset from a photovoltaic power plant in Ningxia,the EGO-CEEMDAN-VMD-BiGRU model was compared with the BiGRU,VMD-BiGRU,and CEEMDAN-VMD-BiGRU models.Under three distinct weather conditions,the average MAE was reduced by 68.93%,55.84%,and 44.56%,respectively,while the RMSE was decreased by 68.23%,53.38%,and 41.03%,respectively.The experimental results demonstrate that the proposed photovoltaic power prediction model exhibits high accuracy and stability,thereby holding practical significance for ensuring the safe and reliable operation of power systems.关键词
光伏发电/预测模型/自适应噪声完全集合经验模态分解/K均值聚类/变分模态分解/鳗鱼-石斑鱼优化算法/双向门控循环单元Key words
photovoltaic generation/prediction model/complete ensemble empirical mode decomposition with adaptive noise/K-means clustering/variational mode decomposition/eel and grouper optimization algorithm/bidirec-tional gated recurrent unit分类
信息技术与安全科学引用本文复制引用
王玲芝,李晨阳,李程..基于EGO-CEEMDAN-VMD-BiGRU模型的短期光伏发电功率预测方法[J].湖南大学学报（自然科学版）,2025,52(12):100-112,13.基金项目
国家自然科学基金资助项目(62073259,52177194),National Natural Science Foundation of China(62073259,52177194) （62073259,52177194）
