Using machine learning algorithms to analyze energy consumption data and optimize management processes at smart enterprises

Abstract

The article explores the transformative potential of machine learning (ML) algorithms in optimizing energy management within smart enterprises. Amidst growing global energy demands and the pressing need for sustainability, ML emerges as a crucial technology for enhancing operational efficiency and reducing environmental impact. Through a comprehensive literature review and practical case studies, the article delves into the application of ML across various sectors, including manufacturing, retail, and data centers, illustrating its capacity to analyze complex energy consumption data, predict future needs, and optimize resource use. Key themes include the integration of ML with existing energy management systems, the advantages and challenges of deploying ML algorithms, and the potential for ML to revolutionize traditional energy management practices. Case studies from leading corporations such as Toyota, Walmart, Google, and Siemens demonstrate tangible benefits, such as predictive maintenance, HVAC optimization, and substantial energy savings, highlighting ML's role in advancing corporate sustainability goals. The article underscores the importance of cross-sectoral collaboration, data quality, and continuous algorithm refinement in realizing ML’s full potential in energy management. Looking forward, it outlines prospects for broader adoption, advanced predictive analytics, autonomous systems, and enhanced sustainability efforts. In summary, this exploration of ML in energy management showcases the technology's significant promise for smart enterprises seeking to navigate the complexities of modern energy use, offering a roadmap toward more sustainable, efficient, and intelligent energy management solutions.