基于VMD-ISSA-KELM的短期光伏发电功率预测

针对光伏发电功率存在随机性和波动性较强、预测精度较低的问题，提出了一种基于变分模态分解(variational mode decomposition, VMD)和改进松鼠觅食算法优化核极限学习机(improved squirrel search algorithm optimization kernel extreme learning machine, ISSA-KELM)的预测模型。首先，利用高斯混合模型(Gaussian mixture model, GMM)将光伏发电功率数据进行聚类，得到不同天气类型下的相似日样本。其次，利用VMD对原始光伏发电功率序列进行平稳化处理，得到若干个规律性较强的子序列。然后，对不同子序列构建KELM预测模型，并使用ISSA优化KELM的核参数和正则化系数。最后，将不同子序列的预测值进行重构，得到最终预测结果。结合实际算例，结果表明：所提出的VMD-ISSA-KELM模型在不同天气条件下均能得到满意的预测精度，且明显优于其他模型，验证了其有效性和优越性。

Short-term photovoltaic power generation prediction based on VMD-ISSA-KELM

There is a problem of a strong randomness, volatility and low prediction accuracy for photovoltaic power generation. Thus a prediction model based on variational mode decomposition (VMD) and an improved squirrel search algorithm optimization kernel extreme learning machine (ISSA-KELM) is proposed. First, photovoltaic power data is clustered using a Gaussian mixture model to obtain similar samples under different weather types. Second, the original photovoltaic power generation power sequence is stabilized using VMD to obtain a number of regular subsequences. Then, the KELM prediction model is constructed for different subsequences and ISSA is used to optimize nuclear and regularization parameters of the KELM. Finally, the predicted value of different subsequences is reconstructed to obtain the final prediction result. Combined with an actual example, the results show that the proposed VMD-ISSA-KELM model can obtain satisfactory prediction accuracy in different weather conditions, and is significantly better than other models, verifying its effectiveness and superiority. This work is supported by the Natural Science Basic Research Program of Shaanxi Province (No. 2021JM-393).