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  • Predicting Solar PV Output based on Hybrid Deep Learning and Physical Models: Case Study of Morocco

    descriptionPublicationkeyboard_double_arrow_right Article 2025Publisher:Bentham Science Publishers Ltd.
    Authors: Samira Abousaid; Loubna Benabbou; Hanane Dagdougui; Ismail Belhaj; +2 Authors

    doi: 10.2174/0123520965264083230926105355

    Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

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    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
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Advanced search in Research products
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The following results are related to Energy Research. Are you interested to view more results? Visit OpenAIRE - Explore.
1 Research products
download

  • Predicting Solar PV Output based on Hybrid Deep Learning and Physical Models: Case Study of Morocco

    descriptionPublicationkeyboard_double_arrow_right Article 2025Publisher:Bentham Science Publishers Ltd.
    Authors: Samira Abousaid; Loubna Benabbou; Hanane Dagdougui; Ismail Belhaj; +2 Authors

    doi: 10.2174/0123520965264083230926105355

    Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

    Claim

    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
    Access Routes
    bronze
    0
    citations0
    popularityAverage
    influenceAverage
    impulseAverage
    BIP!Powered by BIP!
    more_vert
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