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 halfday 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.

Title

Energy Exploration & Exploitation

Publisher

SAGE Publications

Publisher Country

United States of America

Indexing

Thomson Reuters

Impact Factor

2.139

Publication Type

Both (Printed and Online)

Volume

--

Year

2021

Pages

--