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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.
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“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]
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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 ›
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