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Enhancing machine learning model for early warning in PV plants: air temperature prediction informed by power temperature coefficient

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Abstract

This study addresses the need for improved Air Temperature (AT) forecasting during extreme thermal conditions, as it is crucial due to the major influence such conditions exert on various activities. However, an integrated hybrid model is proposed, leveraging the capabilities of neural networks to enhance predictive accuracy and Machine Learning (ML) techniques to assess Photovoltaic (PV) installation risks, using the Power Temperature Coefficient for vigilance during extreme thermal events. Neural networks are designed to capture complex patterns and dependencies in the data, enabling accurate forecasts of upcoming AT fluctuations; while, ML techniques are adapted to determine the specific hotspot where the photovoltaic plant is situated. The obtained results indicate that the Air Temperature Forecaster through Recursive Model-based Convolutional Neural Network (ATFRM-CNN) consistently excels in medium- to long-term predictions (36–72 h), achieving high correlation coefficient (r) values (r = 97.29–94.90%) and a low mean absolute error of 1.168 °C for forecasts up to 72 h. The ATFRM-based Recurrent Neural Network (ATFRM-RNN) and ATFRM-based Gated Recurrent Unit (ATFRM-GRU) models perform best for short-term predictions (6 h) but exhibit a significant decline in accuracy over longer prediction horizons. Additionally, the results of classification techniques in assessing the risks associated with PV plants demonstrate that Gradient Boosting and Random Forest models exhibit exceptional performance, achieving 98% accuracy, surpassing the other classification models. In contrast, Support Vector Machines underperform with an accuracy of 52%.

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Data availability

Authors do not have permission to share the datasets used. The data can be available on request from the Moroccan General Directorate of Meteorology (MGDM, https://www.marocmeteo.ma, last accessed on 20 November 2024).

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Optoelectronics and Applied Energy Techniques, Faculty of Science and Technology, Moulay Ismail University of Meknes, Errachidia, Morocco

    Mohamed Khala, Naima El Yanboiy, Ismail Elabbassi, Mohammed Halimi & Choukri Messaoudi

  2. Materials and Modelling Laboratory, Department of Physics, Faculty of Science, Moulay Ismail University of Meknes, Meknes, Morocco

    Omar Eloutassi

  3. New Energies and Materials Engineering, Faculty of Science and Technology, Moulay Ismail University of Meknes, Errachidia, Morocco

    Youssef El Hassouani

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  1. Mohamed Khala
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  2. Naima El Yanboiy
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Contributions

MK was involved in conceptualization and methodology, writing the original draft; MH, NE, and IE contributed to conceptualization and writing—review and editing; MK and YE analyzed conceptualization and performed formal analysis. CM was involved in data curation and visualization. OE contributed to validation, writing—review and editing, and supervision.

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Correspondence to Mohamed Khala.

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Khala, M., El Yanboiy, N., Elabbassi, I. et al. Enhancing machine learning model for early warning in PV plants: air temperature prediction informed by power temperature coefficient. J Supercomput 81, 394 (2025). https://doi.org/10.1007/s11227-024-06909-w

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