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

Authors

  • Ahmmod Musa Sazib Independent University Bangladesh
  • Joynul Arefin East West University
  • Sabab Al Farabi Lamar University
  • Fozlur Rayhan Lamar University
  • Md Asif Karim Lamar University
  • Shamima Akhter Uttara University

DOI:

https://doi.org/10.32996/jcsts.2025.7.2.5

Keywords:

SHAP, LIME, DeepLIFT, Renewable Energy, Sustainability, Explainable AI, Interpretability

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.

References

[1] Forhad, S., Zakaria Tayef, K., Hasan, M., Shahebul Hasan, A.N.M., Zahurul Islam, M., Riazat Kabir Shuvo, M. (2023). An Autonomous Agricultural Robot for Plant Disease Detection. In: Hossain, M.S., Majumder, S.P., Siddique, N., Hossain, M.S. (eds) The Fourth Industrial Revolution and Beyond. Lecture Notes in Electrical Engineering, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-19-8032-9_50

[2] Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022). Explainable artificial intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy and AI, 9, 100169. https://doi.org/10.1016/j.egyai.2022.100169

[3] S. Forhad, M. S. Hossen, I. A. Ahsan, S. Saifee, K. N. I. Nabeen and M. R. K. Shuvo, "An Intelligent Versatile Robot with Weather Monitoring System for Precision Agriculture," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-7, https://doi.org/10.1109/ISCON57294.2023.10112101

[4] M. A. Iqbal, T. Riyad, M. S. S. Oyon, M. S. Alam, S. Forhad and A. Shufian, "Modeling and Analysis of Small-Scale Solar PV and Li-ion Battery-based Smartgrid System," 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 2024, pp. 1-6, https://doi.org/10.1109/ICAEEE62219.2024.10561824

[5] Forhad, S., Hossen, M. S., Noman, S., Diba, I. A., Mahmud, F., Ullah, M. O., Hossain, S., & Shuvo, M. R. K. (2024). Influence of a Dual Axis IoT- Based Off-Grid Solar Tracking System and Wheatstone Bridge on Efficient Energy Harvesting and Management. Journal of Engineering Research and Reports, 26(3), 125–136. https://doi.org/10.9734/jerr/2024/v26i31099

[6] Ahmad, S. et al. (2024). Simulated Design of an Autonomous Multi-terrain Modular Agri-bot. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-99-3932-9_30

[7] P. Chowdhury, S. Forhad, M. F. Rahman, I. J. Tasmia, M. Hasan and N. -U. -R. Chowdhury, "Feasibility Assessment of an Off-grid Hybrid Energy System for a Char Area in Bangladesh," 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), Rajshahi, Bangladesh, 2024, pp. 1-5, https://doi.org/10.1109/PEEIACON63629.2024.10800194

[8] A. I. Sumaya, S. Forhad, M. A. Rafi, H. Rahman, M. H. Bhuyan and Q. Tareq, "Comparative Analysis of AlexNet, GoogLeNet, VGG19, ResNet50, and ResNet101 for Improved Plant Disease Detection Through Convolutional Neural Networks," 2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), Mt Pleasant, MI, USA, 2024, pp. 1-6, https://doi.org/10.1109/AIBThings63359.2024.10863407

[9] Tasnim, J. et al. (2025). Mobile Applications in Electronic-Healthcare: A Case Study for Bangladesh. In: Namasudra, S., Kar, N., Patra, S.K., Taniar, D. (eds) Data Science and Network Engineering. ICDSNE 2024. Lecture Notes in Networks and Systems, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-97-8336-6_26

[10] S. Forhad et al., "DeepSegRecycle: Deep Learning and ImageProcessing for Automated Waste Segregation and Recycling," 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 2024, pp. 1-6, https://doi.org/10.1109/ICAEEE62219.2024.10561709

[11] S. H. Eshan et al., "Design and Analysis of a 6G Terahertz Aeronautical Antenna Based on Graphene," 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh, 2024, pp. 1-6, https://doi.org/10.1109/ICAEEE62219.2024.10561643

[12] A. Rashid, P. Biswas, A. Biswas, M. A. A. Nasim, K. D. Gupta and R. George, "Present and Future of AI in Renewable Energy Domain: A Comprehensive Survey", arXiv: arXiv:2406.16965., Jun. 2024.

[13] Biswas, P., Rashid, A., Biswas, A. et al. AI-driven approaches for optimizing power consumption: a comprehensive survey. Discov Artif Intell 4, 116 (2024). https://doi.org/10.1007/s44163-024-00211-7

[14] M. A. A. Nasim, P. Biswas, A. Rashid, A. Biswas, and K. D. Gupta, "Trustworthy XAI and Application", arXiv: arXiv:2410.17139., Oct. 2024.

[15] S. Saif, M. A. A. Nasim, P. Biswas, A. Rashid, M. M. A. Haque, and M. Z. B. Jahangir, "Principles and Components of Federated Learning Architectures", arXiv: arXiv:2502.05273., Feb. 2025.

[16] P. Biswas et al., "An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies," in IEEE Access, https://doi.org/10.1109/ACCESS.2025.3537651

[17] A. Rashid, P. Biswas, A. A. Masum, M. A. A. Nasim, and K. D. Gupta, "Power Plays: Unleashing Machine Learning Magic in Smart Grids", arXiv: arXiv:2410.15423., Oct. 2024.

[18] G. He, N. Aishwarya, and U. Gadiraju, "Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant", arXiv: arXiv:2501.17546., Jan. 2025.

[19] Ersoz, B., Sagiroglu, S., Bulbul, H.I.: Methods of explainable artifcial intelligence, trustworthy artifcial intelligence and interpretable machine learning in renewable energy. Int. J. Smart Grid ijSmartGrid 6(4), 136–143 (2022)

[20] B. Ersöz, Ş. Sağıroğlu and H. İ. Bülbül, "A Short Review on Explainable Artificial Intelligence in Renewable Energy and Resources," 2022 11th International Conference on Renewable Energy Research and Application (ICRERA), Istanbul, Turkey, 2022, pp. 247-252, doi: 10.1109/ICRERA55966.2022.9922870.

[21] Perr-Sauer, Jordan and Ugirumurera, Juliette and Gafur, Jamil and Bensen, Erik A. and Nguyen, Truc and Paul, Shuva and Severino, Joseph and Nag, Ambarish and Vijayshankar, Sanjana and Gasper, Paul and Finegan, Donal and Holden, Jacob and Mueller, Juliane and Graf, Peter and Tripp, Charles and Egan, Hilary, Applications of Explainable Artificial Intelligence in Renewable Energy Research: A Perspective from the United States National Renewable Energy Laboratory. Available at SSRN: https://ssrn.com/abstract=5031330 or http://dx.doi.org/10.2139/ssrn.5031330

[22] S. H. Eshan et al., "X band On-body Antenna Design for Lung Cancer Detection using Single-Walled Carbon Nanotubes," 2023 8th International Conference on Robotics and Automation Engineering (ICRAE), Singapore, Singapore, 2023, pp. 182-186, https://doi.org/10.1109/ICRAE59816.2023.10458599

[23] Nallakaruppan, M. K., Shankar, N., Bhuvanagiri, P. B., Padmanaban, S., & Khan, S. B. (2024). Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future. Ain Shams Engineering Journal, 15(6), 102740. https://doi.org/10.1016/j.asej.2024.102740

[24] Biswas, P., Rashid, A., Khan, M. S., Paran, M. S., Taluckder, M. S., Sazib, A. M., Riyad, T., & Forhad, S., Smart Vehicle Monitoring System (SVMS) for Road Safety: A Prototype for Monitoring Drowsiness, Alcohol, and Overload. In Proceedings of the 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM-2025) – Signal, Image and Computer Vision Track.

[25] T. Mim, Z. Mosarat, M. S. Taluckder, A. A. Masum, A. B. Shoumi, M. R. K. Shuvo, S. Forhad, and M. K. Morol, "Myocardial Infarction Prediction: A Comparative Analysis of Supervised Machine Learning Algorithms for Early Detection and Risk Stratification," in Proc. 2nd Int. Conf. Next-Gen. Comput., IoT Mach. Learn. (NCIM-2025), Signal, Image and Computer Vision Track, Paper ID 497, Feb. 2025.

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Published

2025-04-05

Issue

Vol. 7 No. 2

Section

Research Article

How to Cite

Sazib, A. M. ., Arefin, J. ., Farabi, S. A. ., Rayhan, F. ., Karim, M. A. ., & Akhter, S. (2025). Advancing Renewable Energy Systems through Explainable Artificial Intelligence: A Comprehensive Review and Interdisciplinary Framework. Journal of Computer Science and Technology Studies, 7(2), 56-70. https://doi.org/10.32996/jcsts.2025.7.2.5