Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R²) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting.

由于依赖于多变的天气条件，电网中的太阳能集成很复杂。为了解决这个问题，需要不断的研究和开发来确定用于光伏太阳能输出预测的最佳机器学习 (ML) 算法。现有研究已经确立了人工神经网络（ANN）和随机森林（RF）算法在该领域的优越性。然而，最近的研究表明，决策树 (DT)、极端梯度提升 (XGB) 和长短期记忆 (LSTM) 算法在光伏太阳能发电量预测方面具有前景。因此，本研究旨在通过确定这 5 种算法中表现最佳的算法来解决该领域的研究空白。使用了来自美国国家可再生能源实验室 (NREL) 的数据集，其中包括佛罗里达州可可市单晶硅光伏模块的天气参数和太阳能输出数据。预测分数的比较表明，ANN 算法更胜一筹，因为 ANN16 模型产生了最佳的平均绝对误差 (MAE)、均方根误差 (RMSE) 和决定系数 (R ² ) 的值分别为 0.4693、0.8816 W 和 0.9988。得出的结论是，人工神经网络是光伏太阳能发电量预测最可靠和适用的算法。