article · Energy & Fuels
Machine learning methods increasingly support optimisation and predictive modelling across renewable energy systems. However, scaling these technologies requires accountability, which conventional black-box models struggle to deliver due to their lack of transparency and poor interoperability. Explainable artificial intelligence provides a viable alternative by making model operations comprehensible. Incorporating explainability into renewable energy systems offers significant opportunities to improve system performance, efficacy, and operational management, potentially reshaping energy production and consumption patterns. Nevertheless, key challenges persist regarding accountability, fairness, and broader societal or ethical concerns. Addressing these hurdles necessitates further research alongside the creation of standardised datasets and evaluation metrics to ensure renewable energy systems remain sustainable and trustworthy.
Renewable energy grids depend on automated predictions to manage generation and demand efficiently. When artificial intelligence makes decisions that human operators cannot understand, system reliability and safety can be compromised. Introducing explainable artificial intelligence ensures that automated energy operations remain transparent, accountable, and fair, which is essential for building public and institutional trust as clean energy infrastructure expands.
Explainable artificial intelligence tools could support renewable energy operators and system managers looking to optimise power production and consumption. Because the field faces unaddressed barriers regarding fairness, accountability, and missing standardised datasets and evaluation metrics, the application appears to be at an early research stage rather than near commercial market deployment.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1021/acs.energyfuels.3c04343
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
