Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
HUTECH University · Ho Chi Minh City University of Technology · +10 more institutions
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…
Citation impact
- FWCI
- 34.57
- Percentile
- 100%
- References
- 190
Authors
8- VNVan Nhanh Nguyen
HUTECH University, Ho Chi Minh City University of Technology
- WTW. Tarełko
Gdańsk University of Technology
- PSPrabhakar SharmaCorresponding
Delhi Skill and Entrepreneurship University
- AEA.S. El-Shafay
Prince Sattam Bin Abdulaziz University, Mansoura University
- WCWei‐Hsin ChenCorresponding
Tunghai University, National Chin-Yi University of Technology, National Cheng Kung University
Topics & keywords
- Transparency (behavior)
- Renewable energy
- Computer science
- Accountability
- Interoperability
- Work (physics)
- Risk analysis (engineering)
- Environmental economics