现代电力2026，Vol.43Issue(2)：235-243,9.DOI:10.19725/j.cnki.1007-2322.2023.0423
基于自适应噪声完备集合经验模态分解-样本熵-双向长短期记忆网络的短期光伏功率预测
Short-term Photovoltaic Power Prediction Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise-sample Entropy-bidirectional Long Short-term Memory
摘要
Abstract
Photovoltaic power generation exhibits the characteristics of intermittence and great fluctuation,posing challenges for the traditional single model to achieve accurate prediction.Therefore,a prediction model is proposed based on a combination of complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),sample entropy(SE)and bi-directional long-short-term memory(Bi-LSTM).Firstly,the historical power sequences are decomposed using CEEMDAN to mitigate its non-stationarity.The subsequent sequences are reorganized by incorporating sample entropy to address the issue of increased data size in subsequent prediction after the decomposition.Secondly,the reorganized sequences are fed into a Bi-LSTM network for training and prediction.Finally,the final prediction results are obtained by linearly summing up the prediction results of each reorganized sequence.The case validation demonstrates that the constructed combined model is suitable for PV power prediction under diverse weather conditions and exhibits higher prediction accuracy.关键词
光伏功率预测/自适应噪声完备集合经验模态分解/样本熵/双向长短期记忆网络Key words
photovoltaic power prediction/complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/sample entropy(SE)/bi-directional long-short-term memory(Bi-LSTM)分类
信息技术与安全科学引用本文复制引用
潘若宽,竺筱晶..基于自适应噪声完备集合经验模态分解-样本熵-双向长短期记忆网络的短期光伏功率预测[J].现代电力,2026,43(2):235-243,9.基金项目
国家自然科学基金项目(12271342).Project Supported by National Natural Science Foundation of China(12271342). （12271342）
