Machine-Learning Forecasting and Maintenance Decision Support for Low-Carbon Energy Systems

Abstract

The global transition toward sustainable power generation has fundamentally accelerated the integration of low-carbon energy systems, predominantly wind and solar power, into the primary electrical grid. However, the intrinsic volatility and weather-dependent nature of these renewable sources present substantial challenges for grid stability, energy dispatch, and asset management. This paper provides a comprehensive investigation into the application of machine learning algorithms for both generation forecasting and predictive maintenance decision support within low-carbon energy infrastructures. By synthesizing advanced predictive modeling techniques with reliability engineering, the research demonstrates how data-driven paradigms can simultaneously optimize power output predictions and minimize unexpected equipment failures. We explore the architectural requirements for high-fidelity data acquisition, evaluate the comparative efficacy of deep learning methodologies such as long short-term memory networks and transformer architectures, and analyze their integration into holistic maintenance frameworks. The analysis reveals that unified decision support systems, which concurrently leverage generation forecasts and health degradation models, significantly reduce operational and maintenance costs while maximizing the availability of energy assets. Through detailed theoretical discourse and operational scenario evaluations, this study underscores the critical necessity of transitioning from traditional, reactive operational paradigms toward proactive, intelligent management ecosystems in the pursuit of secure and economically viable low-carbon energy networks.