Publications Repository - Gdańsk University of Technology

Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimizationand model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systemsneeds a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work isdifficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability inblack-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potentialadvantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI withRE 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 andethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows thepotential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to targetthe efficient energy-use goals for a more sustainable and trustworthy future.

Authors

• Van Nhanh Nguyen Nhanh Van,
• prof. dr hab. inż. Wiesław Tarełko link open in new tab ,
• Sharma Prabhakar,
• Ahmed Shabana El-Shafay,
• Wei-Hsin Chen,
• Phuoc Quy Phong Nguyen,
• Nguyen Xuan Phuong,
• Tuan Anh Nguyen