The DOI matches its Crossref record; 190 of the 190 references it cites were found in a registry or an index.
Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
Abstract
Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The 'black box' nature of complex AI models hinders understanding of their decisionmaking, a critical barrier in high-stakes energy applications [25,26].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“The focus has been shifting from initial implementation hurdles toward ensuring the Trustworthiness and transparency of deployed systems, as seen in the growing body of work on XAI [25,26].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“Lack of transparency (XAI, Ethics; Sections 3.1.4and 3.4.2),fears of job displacement (Section 3.4.1),data privacy concerns (Sections 3.1.3and 3.4.2) [22,65], perceptions of unfairness/bias [26], and concerns about AI's environmental/safety impacts (Section 3.2.4)can fuel opposition.”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“Biased AI, trained on historical data reflecting societal inequities [26,63], could lead to discriminatory outcomes in resource allocation or pricing, eroding public trust (Section 3.4.3).”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
In the citing paper
“a practice increasingly called for in energy justice research [26,64]”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [26]
Citations
Cites
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
A review of deep learning for renewable energy forecasting
Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.111799 · Cited by 1,211
A comprehensive study of renewable energy sources: Classifications, challenges and suggestions
Ang, T.-Z., Salem, M., Kamarol, M., Das, H. S., Nazari, M. A., & Prabaharan, N. (2022) · Energy Strategy Reviews · doi:10.1016/j.esr.2022.100939 · Cited by 1,151
Rahman, A., Farrok, O., & Haque, M. M. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112279 · Cited by 1,105
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
Adebayo, T. S., & Kırıkkaleli, D. (2021) · Environment Development and Sustainability · doi:10.1007/s10668-021-01322-2 · Cited by 599
Prediction of solar energy guided by pearson correlation using machine learning
Jebli, I., Belouadha, F.-Z., Kabbaj, M. I., & Tilioua, A. (2021) · Energy · doi:10.1016/j.energy.2021.120109 · Cited by 590
Olu-Ajayi, R., Alaka, H., Sulaimon, I. A., Sunmola, F. T., & Ajayi, S. O. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.103406 · Cited by 550
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Efficiency of geothermal power plants: A worldwide review
Zarrouk, S. J., & Moon, H. (2014) · Geothermics · doi:10.1016/j.geothermics.2013.11.001 · Cited by 535
Research works printed but not linked: 1
- Spreitzer, N.; Haned, H.; van der Linden, I. Evaluating the Practicality of Counterfactual Explanations NeurIPS 2022 Work. TSRML ; 2022; p 11.
Cited by
Sorted by citations
Items 101–130 of 130
Optimizing the Biodiesel Production from Waste Cooking Oil Employing Gray Wolf Optimization
Le, V. V., Duong, M. T., Nguyen, D. C., Chau, T. H., Paramasivam, P., & Nguyen, P. Q. P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6124-4_10
Self-citationShort-Term Residential Load Forecasting Using Textual Embeddings-Based Multi-encoder Transformer
Raghavan, V. S., & Vijayaraghavulu, G. A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10710-z
Wu, X., Liu, W., Chen, L., Lin, J., & Zhao, J. (2025) · Energy & Environment · doi:10.1177/0958305x251392870
Artificial intelligence in renewable energy technologies and sustainable transition
Fareed, B., Vasić, L., Sher, F., Clauser, N. M., Soltani, M., & Sofianos, M. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33771-0.00023-x
Sahu, N., Azad, C., & Kumar, U. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126691
Afterglow-Engineered Monstera Enhances Photosynthetic Carbon Sequestration
Chen, Y.-A., Sharma, A. K., Pan, F., & Su, Y. (2026) · ACS ES&T Engineering · doi:10.1021/acsestengg.5c00891
Hrithik, P. M., Khan, M. S., Azad, I., Hisam, M. W., Dar, A. A., & Smerat, A. (2026) · Clean Energy Science and Technology · doi:10.18686/cest530
Explainable Artificial Intelligence Driven Modeling of Syngas Yield in Biomass Gasification
Nguyen, T. T. H., Duong, M. T., Nguyen, V. Q., Dang, T. N., Do, P. N., & Chau, T. H. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14194-1_3
Self-citationExplainable Machine Learning Based BioDiesel Synthesis from Used Frying Oil
Nguyen, T. T. H., Cao, D. N., Truong, T. H., Nguyen, A. Q., & Trần, V. D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14194-1_1
Altalbawy, F. M. A., Alfilh, R. H. C., Vora, T. P., Yadav, A., Manjunatha, R., Thakur, R., Angel, B., Das, P., Negi, A. S., Norberdiyeva, M., et al. (2026) · Polymers for Advanced Technologies · doi:10.1002/pat.70493
Machine Learning as a Catalyst in Advanced Materials Research and Development
Akram, M., & Choudhry, W. U. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3556-8.ch003
Khouili, O., Hanine, M., & Louzazni, M. (2026) · IEEE Power Electronics Magazine · doi:10.1109/mpel.2025.3577569
Underused geothermal potential in Southeast Asia’s net-zero transition
Ramadhan, R., & Mon, M. T. (2026) · Geoscience Frontiers · doi:10.1016/j.gsf.2026.102331
Aravinda, D., Shivanand, H. K., Nagaraja, S., Hadi, A. A., Naik, N. C. K., Khan, M. A., & Emma, A. F. (2026) · Journal of Materials Science Materials in Engineering · doi:10.1186/s40712-026-00456-3
Yadav, K., Alkwai, L. M., Almansour, S., & Mottaghi, M. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102047
Chang, Y., An, A., Luo, Z., Liu, X., & Zhang, H. (2026) · Energy & Fuels · doi:10.1021/acs.energyfuels.6c00394
Explainable ML – Based Photovoltaic Power Prediction under Climatic Variability
Balakrishnan, D. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202672101004
Shin, Y.-U., Yoon, K., Kwon, E. E., & Song, H. (2026) · International Journal of Energy Research · doi:10.1155/er/9050367
Wind Energy Prediction Utilizing Artificial Intelligence on Green Energy Transformation
Verma, V., Sarkar, T., & Rakhra, M. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898815042126010008
Abbas, N., Rasool, S. G., Mahmood, S., & Nadeem, M. (2026) · Qlantic journal of social sciences. · doi:10.55737/qjss.vii-ii.26511
Haddar, M., Hammami, A., Mohameden, C. B., Mohamed-Yahya, A., Mahmoud, A. K., & Haddar, M. (2026) · Heat Transfer · doi:10.1002/htj.70351
Al-Mutiry, M., Balla, H. H. A., Khlifi, M. A., Haji, B. S., Althobaiti, M. A. O., Althbiti, A., Alsairy, N., Abu-Zinadah, H., Nguyen, D. C., & Mahariq, I. (2026) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2026.112469
Hu, X., Yin, T., Tian, P., Zhao, J., Wang, X., Chen, J., Zhang, H., Sun, J., Qian, Y., & Di, F. (2026) · Applied Sciences · doi:10.3390/app16189207
Thermal performance, challenges and applications on water-based hybrid nanofluids: A complete review
Mohanta, A., Jena, S., Mishra, S. R., & Panda, S. (2026) · Chemical Thermodynamics and Thermal Analysis · doi:10.1016/j.ctta.2026.100381
An AI-driven framework for property-oriented fuel design toward sustainable engine combustion
Liu, Y., Li, A., Li, Y., Fan, Y., Qi, Y., Zhang, C., Zhu, L., & Huang, Z. (2026) · iScience · doi:10.1016/j.isci.2026.117677
Ikedo, T., & YAMASAKI, Y. (2026) · International Journal of Engine Research · doi:10.1177/14680874261487974
Sulaima, M. F., Arora, S., Krejcar, O., Herout, L., & Namazi, H. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100869
Advancements and challenges in perovskite solar cells: a comprehensive review
Dar, S. A., Sengar, B. S., & Gahlawat, D. (2026) · Next research. · doi:10.1016/j.nexres.2026.102547
Artificial intelligence for renewable‑dominated energy systems: A scoping review
Raihan, A., Rahman, A., Haddad, K., Hellany, A., Hasan, K. N., & Alim, M. A. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102385
Artificial intelligence for thermal and exergy optimization of steam cycle power plants: a review
Ahmed, F., & Mewada, H. K. (2026) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-026-16477-1
Metadata
- Authors
- Nguyen, V. N., Tarełko, W., Sharma, P., El-Shafay, A. S., Chen, W.-H., Nguyen, P. Q. P., Nguyen, X. P., & Hoang, A. T.
- Affiliations
- HUTECH University (VN)
- Gdańsk University of Technology (PL)
- Delhi Skill and Entrepreneurship University (IN)
- Prince Sattam Bin Abdulaziz University (SA)
- Tunghai University (TW)
- Ho Chi Minh City University of Transport (VN)
- PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City 700000, Vietnam
- Dong A University (VN)
- Corresponding author
- Sharma, Prabhakar
- Chen, Wei-Hsin
- Hoang, Anh Tuan
- ORCID
- Venue
- Energy & Fuels
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-01-19
- Volume
- 38
- Issue
- 3
- Pages
- 1692-1712
- ISSN
- 0887-0624 (ISSN-L)
- 1520-5029
- OpenAlex
- W4391035178
- Cited by
- 130
- References
- 190
- Citations by year
- 2026: 44 · 2025: 52 · 2024: 34
- FWCI
- 19.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Energy & Fuels (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Keywords
- explainable AI, renewable energy, black-box models, transparency, accountability, fairness, ethical implications, energy system management, energy use efficiency, model interpretability, evaluation metrics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
