Industrial Energy Management System: Design of a Conceptual Framework Using IoT and Big Data - Mehar Ullah; Arun Narayanan; Annika Wolff; Pedro H. J. Nardelli (IEEE Access, 2022)
Industrial activities consume a large portion of the total energy demand worldwide and thus significantly contribute to greenhouse gas emissions. One of the most effective ways to reduce energy consumption in the industrial sector is to implement an energy management system. Current research into Industrial Energy Management System (IEnMS) remains insufficient, and to the best of our knowledge, a holistic framework for an IEnMS using the Internet of Things (IoT) and big data does not exist. This paper provides a comprehensive systematic literature review of the existing academic publications on IEnMS from where the main requirements and components of an IEnMS are identified. We further verify this study by conducting a detailed survey with specialized employees of ten (10) large companies to acquire expert opinion about using the modern technologies like IoT, big data, and data analytics in IEnMS. We have then proposed a theoretical framework for the IEnMS using IoT, big data and data analytics to construct an effective cyber-physical system architecture including steps from data acquisition to the end-user decision-making process. These findings demonstrate how the suggested framework provides an objective methodology for selecting the most appropriate IEnMS for various businesses based on their specific needs.