为提高光伏发电功率的预测精度，减少光伏发电对电网调度的影响，提出一种基于相似日聚类的PCC-EEMD-BES-ELM光伏发电功率短期预测模型。通过Pearson相关系数（Pearson correlation coefficient, PCC）筛选出主要因素作为预测模型的输入，集成经验模态分解（ensemble empirical mode decomposition, EEMD）能够提取出光伏发电功率的多层次特征，秃鹰搜索算法（bald eagle search, BES）能有效选择出重要特征并进行模型优化，而优化极限学习机（extreme learning machine, ELM）则通过快速的学习机制进行高效的预测，从而形成一个能够在复杂环境下提供高精度预测的强大模型。最后利用相似日聚类将天气分成晴天、雨天、多云分别进行发电功率预测。以实际的光伏电站数据对模型进行验证，结果表明该模型最小的根均方差（root mean square error, RMSE）达到0.048 1，平均绝对误差（mean absolute error, MAE）达到0.035 9，平均相对百分误差（mean absolute percentage error, MAPE）达到0.947%。可见在不同季节不同天气下，该模型均表现出最低的预测误差，证明其在光伏发电功率短期预测中的有效性和稳定性。

In order to improve the accuracy of photovoltaic power prediction and reduce the impact of photovoltaic power generation on grid scheduling, a short-term photovoltaic power forecasting model based on similar day clustering, PCC-EEMD-BES-ELM, was proposed. The PCC (Pearson correlation coefficient) was used to select the main factors as the inputs for the forecasting model. EEMD(ensemble empirical mode decomposition) was employed to extract multi-level features of photovoltaic power generation, BES (bald eagle search) effectively selected important features and optimizes the model, while the optimized ELM (extreme learning machine) performs efficient predictions through its fast learning mechanism, forming a robust model capable of providing high-accuracy forecasts in complex environments. Finally, the similar day clustering method was used to categorize weather into sunny, rainy, and cloudy days for power generation forecasting. The model was validated using real photovoltaic station data, and the results show that the minimum root mean square error (RMSE) reaches 0.048 1, the mean absolute error (MAE) is 0.035 9, and the mean absolute percentage error(MAPE) is 0.947%. The model demonstrates the lowest forecasting errors under various seasons and weather conditions, proving its effectiveness and stability in short-term photovoltaic power prediction.