AI-Driven Energy Management in Smart Factories
Keywords:
artificial intelligence, energy management, smart factory, Industry 4.0, predictive maintenance, edge computing, sustainability, socio-technical systemsAbstract
The convergence of artificial intelligence with industrial energy systems is reshaping the operational logic of smart factories, offering unprecedented opportunities for dynamic load balancing, predictive maintenance, and real-time consumption optimisation. This paper examines the architectural, infrastructural, and governance dimensions of AI-driven energy management within the broader socio-technical context of Industry 4.0. It argues that while machine learning models can significantly reduce energy intensity and carbon footprint, their deployment introduces structural trade-offs between model complexity, interpretability, robustness, and fairness. A system-level perspective is adopted to analyse how data pipelines, edge-cloud hierarchies, and control loops interact with regulatory frameworks and organisational incentives. Through cross-domain comparisons with smart grids and building automation, the paper highlights the unique challenges of industrial environments, including heterogeneous sensor networks, variable production schedules, and legacy equipment integration. The role of predictive maintenance in energy savings is critically assessed, with attention to data quality, model generalisation, and the risk of optimisation silos. Policy implications are explored, focusing on standardisation, transparency requirements, and the distribution of energy savings across stakeholders. The paper concludes by outlining future research directions that emphasise hybrid intelligence, federated learning, and human-in-the-loop architectures as pathways toward more resilient and equitable energy management systems.
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