New model of photovoltaic system adapted by a digital MPPT control and radiation predictions using deep learning in Morocco agricultural sector

• Autorzy: Zouhri Amal , El Mallahi Mostafa

Identyfikatory

DOI

10.14313/JAMRIS/2‐2023/17

• Języki publikacji: EN

Abstrakty

EN

Solar energy is an essential factor in Moroccan sustainable development, especially in solar pumping in the agricultural sector. It is therefore difficult to dissociate the energy system of a society from its economic development and social development. Solar radiation prediction is useful in giving us a global overview on maintaining the integrity of solar systems. Access to database use makes this process more flexible. Solar forecasts can be generated using various available data sources. There are two major pillars of this data: the exploitation of historical solar radiation data, and the exploitation of other meteorological factors. On the other hand, the choice of data can have an impact on the choice of the model and the approach employed. In this paper we suggest an idea that aims to monitor in real time the situation of solar radiation in Morocco, using Long Short‐Term Memory for deep learning models compared with Artificial Neural Networks and Deep Neural Networks to predict the solar radiation with regard to solar pumping in the Moroccan agricultural sector.

Słowa kluczowe

EN

Morocco sustainable development; solar pumping; Deep Learning; renewable energies; solar radiation

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2023
• Tom: Vol. 17, No. 2
• Strony: 74--84
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Zouhri Amal

• Sidi Mohamed Ben Abdellah University, Faculty of Sciences, Department of Physics, Fez, Morocco

autor

El Mallahi Mostafa

• Sidi Mohamed Ben Abdellah University, High Normal School, Fez, Morocco

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).