Transfer Learning for Renewable Energy Systems: A Survey

Rami Al-Hajj (), Ali Assi, Bilel Neji, Raymond Ghandour and Zaher Al Barakeh
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Rami Al-Hajj: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Ali Assi: Independent Researcher, Montreal, QC H1X1M4, Canada
Bilel Neji: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Raymond Ghandour: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Zaher Al Barakeh: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Sustainability, 2023, vol. 15, issue 11, 1-28

Abstract: Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives.

Keywords: transfer learning; knowledge transfer; renewable energy; energy forecasting; fault diagnosis; buildings load forecasting; reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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