Open AccessNovel models for photovoltaic output current prediction based on short and uncertain dataset by using deep learning machines
Author(s) -
Tamer Khatib,
Ameera Gharaba,
Zain Haj Hamad,
Aladdin Masri
Publication year - 2021
Publication title -
energy exploration and exploitation
Language(s) - English
Resource type - Journals
eISSN - 2048-4054
pISSN - 0144-5987
DOI - 10.1177/01445987211068119
Subject(s) - photovoltaic system , python (programming language) , artificial neural network , computer science , mean squared error , current (fluid) , predictive modelling , mean squared prediction error , deep learning , test data , artificial intelligence , machine learning , engineering , mathematics , statistics , electrical engineering , operating system , programming language
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.