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Advancing Renewable Energy Systems through Explainable Artificial Intelligence: A Comprehensive Review and Interdisciplinary Framework

Author

Listed:
  • Ahmmod Musa Sazib

    (Bachelor of Science in Electrical and Electronics Engineering, Independent University Bangladesh, Dhaka, Bangladesh)

  • Sabab Al Farabi

    (Master of Science in Industrial and System Engineering, Lamar University, Texas, USA)

  • Fozlur Rayhan

    (Master of Science in Electrical and Computer Engineering Department, Lamar University, Texas, USA)

  • Md Asif Karim

    (Master of Science in Engineering Management, Lamar University, Texas, USA)

Abstract

Explainable Artificial Intelligence (XAI) plays a pivotal role in advancing transparency, reliability, and informed decision-making in renewable energy systems. This review provides a comprehensive analysis of state-of-the-art XAI methodologies—including Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Deep Learning Important FeaTures (DeepLIFT), and rule-based models—by critically evaluating their applications, advantages, and limitations within renewable energy research. Despite notable progress, significant challenges persist, including computational inefficiencies, the absence of standardized evaluation metrics, and the inherent trade-off between model accuracy and interpretability. This study proposes a novel interdisciplinary framework that integrates domain-specific XAI methodologies, standardized benchmarking protocols, and collaborative efforts between AI researchers and energy experts. By addressing these challenges, this review aims to facilitate the broader adoption of interpretable and reliable AI-driven solutions for the sustainable advancement of renewable energy systems.

Suggested Citation

  • Ahmmod Musa Sazib & Sabab Al Farabi & Fozlur Rayhan & Md Asif Karim, 2025. "Advancing Renewable Energy Systems through Explainable Artificial Intelligence: A Comprehensive Review and Interdisciplinary Framework," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(2), pages 56-70, March.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:2:id:8841
    DOI: 10.32996/jcsts.2025.7.2.5
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