Academia Open
Vol 10 No 1 (2025): June (In Progress)
Energy

AI Applications for Optimizing Performance and Longevity in Solar Energy Systems
Aplikasi AI untuk Mengoptimalkan Kinerja dan Umur Panjang dalam Sistem Energi Surya


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  • Jaafar Ali Lafta Alnasrawi*,
  • Zaid Makki Jebur
Jaafar Ali Lafta Alnasrawi
1Department of Medical Laboratories, College of Medical and Health Technologies, Ahlulbait University, Iraq *
Zaid Makki Jebur
2Department of Power Mechanics, College of Technical Engineering, AlـFurat AlـAwsat Technical University , Iraq

(*) Corresponding Author
Picture in here are illustration from public domain image or provided by the author, as part of their works
DOI: https://doi.org/10.21070/acopen.10.2025.10829
Published April 17, 2025
Keywords
  • Solar Energy,
  • Artificial Intelligence,
  • Machine Learning,
  • Predictive Maintenance,
  • System Optimization
How to Cite
Alnasrawi, J. A. L., & Jebur, Z. M. (2025). AI Applications for Optimizing Performance and Longevity in Solar Energy Systems. Academia Open, 10(1), 10.21070/acopen.10.2025.10829. https://doi.org/10.21070/acopen.10.2025.10829

Copyright (c) 2025 Jaafar Ali Lafta Alnasrawi, Zaid Makki Jebur

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

General background: Solar energy is recognized as the most potent and abundant form of renewable energy available to meet global energy demands. Specific background: Despite its potential, solar power systems face challenges related to low efficiency, high operational costs, and safety concerns. Knowledge gap: These persistent issues require intelligent solutions, yet the integration of advanced artificial intelligence (AI) techniques into solar energy systems remains underexplored in practical and scalable contexts. Aims: This study aims to examine the role of AI—particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL)—in addressing key limitations in solar power systems. Results: We highlight three main AI-driven use cases: performance forecasting, system optimization, and predictive maintenance, all of which significantly improve operational efficiency, reliability, and system longevity. Novelty: By leveraging AI’s adaptive and data-driven capabilities, this work presents an innovative framework for real-time decision-making and predictive analytics in solar energy systems. Implications: The findings underscore AI’s transformative potential in enabling the widespread, flexible, and sustainable integration of solar power into global energy infrastructures, thereby accelerating the transition toward a resilient and intelligent renewable energy future.

Highlights:

 

  1. AI boosts solar performance via forecasting, optimization, and maintenance.

  2. Machine learning adapts systems using data-driven predictive models.

  3. Enhances sustainability by reducing costs and extending system lifespan.

 

Keywords: Solar Energy, Artificial Intelligence, Machine Learning, Predictive Maintenance, System Optimization

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