为解决传统机器学习方法在面对多变的环境因素和不平稳序列时导致光伏功率预测精度低的问题，提出一种基于完全经验模态分解（complete ensemble empirical mode decomposition, CEEMD）和长短期记忆神经网络（long short-term memory, LSTM）相结合的光伏短期功率预测模型。首先，充分考虑影响光伏出力的太阳辐照度、相对湿度、大气压力和空气温度4种环境因素，通过CEEMD将气象因素特征曲线分解为多模态特征数据，准确捕捉其不同的时间尺度和频率特征，进而充分保留环境数据的不平稳特征。其次，在此基础上，利用LSTM网络对多模态特征数据进行时间序列建模，旨在保留时间序列的季节性和不平稳特征，为后续建模提供更准确的输入特征。最后，通过对分解后的信号开展训练，根据输入数据的变化自适应调整预测模型参数，迭代生成特定场景下的预测模型，从而灵活应对实时环境变化，得到相应功率预测结果。在海南一孤立海岛分布式光伏电站37 kW子阵的8个月气象和功率数据集进行验证，实验结果表明，所提方法在保留环境数据细节和局部特性上具有显著优势，在不同气象条件均具有良好的自适应性，有效提高了光伏短期功率预测精度。

To solve the problem of low accuracy in photovoltaic power prediction caused by traditional machine learning methods in the face of changing environmental factors and non-stationary sequences, a photovoltaic short-term power prediction model based on complete empirical mode decomposition (CEEMD) and long short-term memory neural network (LSTM) was proposed. Firstly, four environmental factors affecting photovoltaic output, namely solar irradiance, relative humidity, atmospheric pressure, and air temperature, were fully considered. The meteorological factor characteristic curve was decomposed into multimodal feature data through CEEMD, accurately capturing its different time scales and frequency characteristics, and thus fully preserving the non-stationary characteristics of environmental data. Secondly, based on this, the LSTM network was used to model the time series of multimodal feature data, aiming to preserve the seasonal and non-stationary features of the time series and provide more accurate input features for subsequent modeling. Finally, by training the decomposed signal and adaptively adjusting the prediction model parameters based on changes in input data, the prediction model for specific scenarios was iteratively generated to flexibly respond to real-time environmental changes and obtain corresponding power prediction results. The validation was conducted on an 8-month meteorological and power dataset of a 37 kW sub array distributed photovoltaic power station on an isolated island in Hainan. The experimental results show that the proposed method has significant advantages in preserving environmental data details and local characteristics, and has good adaptability under different meteorological conditions, effectively improving the accuracy of short-term photovoltaic power prediction.