Infrequent adverse event prediction in low carbon energy production using machine learning

TL;DR

This paper presents a machine learning framework for predicting rare adverse events in low-carbon energy production, improving predictive maintenance by handling imbalanced data in critical applications.

Contribution

It introduces a novel framework that leverages multiple classifiers to predict infrequent adverse events in low-carbon energy systems, addressing class imbalance challenges.

Findings

Effective prediction of foam formation in anaerobic digestion.

Successful detection of condenser tube leakage in nuclear power turbines.

Demonstrated robustness of the proposed methods through extensive experiments.

Abstract

We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance. We cast the corresponding machine learning task as an imbalanced classification problem and propose a framework for solving it that is capable of leveraging different classifiers in order to predict the occurrence of an adverse event before it takes place. In particular, we focus on two applications arising in low-carbon energy production: foam formation in anaerobic digestion and condenser tube leakage in the steam turbines of a nuclear power station. The results of an extensive set of omputational experiments show the effectiveness of the techniques that we propose.