Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia
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(2020) 10:34
Energy, Sustainability and Society
ORIGINAL ARTICLE
Open Access
Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia Afef Ben Othman1,2*, Ayoub Ouni2,3 and Mongi Besbes2,4
Abstract Background: Several climatologists and experts in the renewable energy field agree that GHI and DNI calculation models must be revised because of the increasingly unpredictable and powerful climatic disturbances. The construction of analytical mathematical models for the prediction of these disturbances is almost impossible because the physical phenomena relating to the climate are often complex. We raise the question over the current and future PV system’s sustainable energy production and whether climate disturbances will be affecting this sustainability and resulting in supply decline. Methods: In this paper, we tried to use deep learning as a tool to predict the evolution of the future production of any geographic site. This approach can allow for improvements in decision-making concerning the implantation of solar PV or CSP plants. To reach this aim, we have deployed the databases of NASA and the Tunisian National Institute of Meteorology relating to the climatic parameters of the case study region of El Akarit, Gabes, Tunisia. In spite of the colossal amount of processed data that dates back to 1985, the use of deep learning algorithms allowed for the validation of the previously made estimates of the energy potential in the studied region. Results: The calculation results suggested an increase in production as it was confirmed by the 2019 measures. The findings obtained from the case study region were reliable and seemed to be very promising. The results obtained using deep learning algorithms were similar to those produced by conventional calculation methods. However, while conventional approaches based on measurements obtained using hardware solutions (ground sensors) are expensive and very difficult to implement, the suggested new approach is cheaper and more convenient. Conclusions: In the existence of a protracted controversy over the hypothetical effects of climate change, making advances in artificial intelligence and using new deep learning algorithms are critical procedures to strengthening conventional assessment tools of the production sites of photovoltaic energy and CSP plants. Keywords: GHI estimation, Deep learning, PV production prediction, ANN modeling and greenhouse effect
* Correspondence: [email protected] 1 National School of Engineers of Carthage, University of Carthage, Tunis, Tunisia 2 Laboratory of Robotics, Informatics and Complex Systems, University Tunis El Manar, Tunis, Tunisia Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author
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