【摘要】 光伏发电预测对于新型电力系统的平稳运行至关重要。针对光伏发电短期预测，提出了一种融合改进的完全自适应噪声集合经验模态分解(ICEEMDAN)、核主成分分析(KPCA)和改进的食肉植物算法(ICPA)与长短期记忆网络(LSTM)的光伏发电预测方法。首先，该方法通过ICEEMDAN提取气象数据中非线性信号的隐含特征；其次，采用核主成分分析降低分解后产生的冗余信息，并根据主成分贡献率大小选取模型输入参数；最后，对食肉植物算法(CPA)进行改进，构建ICPA-LSTM模型，并开展了晴天、雨天、多云和多变天气4种典型天气类型下光伏发电功率预测校验。结果表明：在不同天气情况下，所提模型的决定系数R~2均大于99%,相较于对照模型具有更好的预测性能。更多还原

【Abstract】 Accurate forecasting of photovoltaic(PV) power is essential for stable operation of new electricity systems. This study proposed a novel approach for short-term PV power forecasting combining the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN), kernel principal component analysis(KPCA) and an improved carnivorous plant algorithm-long short-term memory(ICPA-LSTM) network. First, ICEEMDAN was employed to extract the implicit features of nonlinear signals from the meteorological data. Next, KPCA was performed to reduce the redundancy of the decomposed data and select model input parameters based on the contribution of principal components. Finally, the ICPA-LSTM model was constructed by improving the carnivorous plant algorithm(CPA). The approach was validated for PV power prediction under four typical weather conditions: sunny, rainy, cloudy, and variable weather. Results show that the proposed model reaches a determination coefficient(R~2) of over 99% across all four weather scenarios, and achieves better performance compared to benchmark models.更多还原