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Volume 18, Issue 1
  • ISSN: 2213-1116
  • E-ISSN: 2213-1132

Abstract

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 methods 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.

© 2025 Bentham Science Publishers
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2025-01-01
2024-12-26

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Predicting Solar PV Output based on Hybrid Deep Learning and Physical Models: Case Study of Morocco
Recent Patents on Electrical & Electronic Engineering (Formerly Recent Patents on Electrical Engineering) 18, 6; https://doi.org/10.2174/0123520965264083230926105355
/content/journals/eeng/10.2174/0123520965264083230926105355
/content/journals/eeng/10.2174/0123520965264083230926105355
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  • Article Type:     Research Article
Keyword(s): deep learning; electrical model; hybrid approach; NWP model; power forecasting; PV power
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