Class Imbalance Challenges in Predictive Maintenance for the Energy Power Sector: A Comprehensive Review

Abstract

Imbalanced datasets, characterised by a significant disparity in class distribution, present significant obstacles in data-driven applications within the energy power sector. The presence of class imbalance can lead to the creation of biased models, decreased accuracy, and limited generalisation capabilities. In recent times, there have been several machine learning methodologies suggested to tackle the problem of class imbalance across different domains. These methodologies encompass strategies at multiple levels, such as data level, algorithm level, and the utilisation of oversampling and undersampling techniques. The objective of this study is to present an in-depth review of the current body of research pertaining to machine learning techniques employed in addressing the issue of imbalanced data within the electrical power industry. Specifically, our focus will be on evaluating several benchmarks including data preprocessing, algorithm selection, oversampling, and undersampling methodologies. This study has the potential to contribute to the advancement of more precise and dependable predictive maintenance models within the energy power industry.