Artificial Intelligence-Driven Optimization of Energy Consumption in Industrial Systems: A Systematic Literature Review
Abstract: The increasing demand for energy efficiency in industrial systems has driven significant interest in artificial intelligence (AI) as a transformative tool for optimizing energy consumption. This systematic literature review examines the role of AI in industrial energy optimization, focusing on its applications across key dimensions such as energy management, Industry 4.0, supply chain operations, and emerging technologies. We identify the state-of-the-art AI techniques, their implementation challenges, and the potential for sustainable industrial practices. The review synthesizes findings from a broad range of studies to highlight how AI-driven solutions address energy inefficiencies, reduce operational costs, and align with global sustainability goals. Methodologically, we adopt a structured approach to analyze peer-reviewed literature, categorizing insights into thematic clusters that reflect the interdisciplinary nature of AI applications in industrial energy systems. Results indicate that machine learning, deep learning, and reinforcement learning are prominently employed for predictive maintenance, real-time energy monitoring, and dynamic resource allocation. However, barriers such as data quality, computational complexity, and integration with legacy systems persist. The review concludes by outlining future research directions, emphasizing the need for scalable AI models, cross-domain collaboration, and policy frameworks to accelerate adoption. This work provides a comprehensive foundation for researchers and practitioners aiming to advance AI-enabled energy optimization in industrial contexts.
Keywords: Artificial intelligence (AI); energy; Industry 4.0; global sustainable goals
How to Cite?: Dr. Ringcher Basumatary, "Artificial Intelligence-Driven Optimization of Energy Consumption in Industrial Systems: A Systematic Literature Review", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1949-1955, https://www.ijsr.net/getabstract.php?paperid=SR26928170153, DOI: https://dx.doi.org/10.21275/SR26928170153