This paper presents deep learning neural network models for photovoltaic output current prediction. The proposed models are long short-term memory and gated recurrent unit neural networks. The proposed models can predict photovoltaic output current for each second for a week time by using global solar radiation and ambient temperature values as inputs. These models can predict the output current of the photovoltaic system for the upcoming seven days after being trained by half-day data only. Python environment is used to develop the proposed models, and experimental data of a 1.4 kWp PV system are used to train, validate and test the proposed models. Highly uncertain data with steps in seconds are used in this research. Results show that the proposed models can accurately predict photovoltaic output current whereas the average values of the root mean square error of the predicted values by the proposed LSTM and GRU are 0.28 A and 0.27 A (the maximum current of the system is 7.91 A). In addition, results show that GRU is slightly more accurate than LSTM for this purpose and utilises less processor capacity. Finally, a comparison with other similar methods is conducted so as to show the significance of the proposed models.

本文介绍了用于光伏输出电流预测的深度学习神经网络模型。所提出的模型是长短期记忆和门控循环单元神经网络。所提出的模型可以通过使用全球太阳辐射和环境温度值作为输入来预测一周内每秒的光伏输出电流。这些模型仅通过半天数据训练后，就可以预测未来 7 天光伏系统的输出电流。Python环境用于开发所提出的模型，并使用1.4 kWp光伏系统的实验数据来训练、验证和测试所提出的模型。本研究使用了以秒为单位的高度不确定性数据。结果表明，所提出的模型可以准确地预测光伏输出电流，而提出的LSTM和GRU预测值的均方根误差平均值分别为0.28 A和0.27 A（系统最大电流为7.91 A）。此外，结果表明，在此目的上，GRU 比 LSTM 更准确，并且使用更少的处理器容量。最后，与其他类似方法进行了比较，以表明所提出模型的重要性。