Integration of IoT and AI for Enhanced Efficiency and Control in Smart Energy Management Systems

This paper presents a detailed analysis of how the integration of Internet of Things (IoT) and artificial intelligence (AI) to energy management systems (EMS) has a possibility of enhancing energy efficiency and control across various sectors. Therefore, this paper seeks to analyze the new developments in smart EMS through analyzing the possibilities of the combination of IoT and AI. Hence, the research utilises the hybrid approach with regard to the applicable methodology that includes numerical and analytical description, qualitative case and example analysis, and the actual implementation. Sophisticated quantitative analysis was also performed to analyze how the sensors of IoTs and the AI algorithm would affect the real-time energy monitoring, the demand forecasting, and the automated energy control systems. This paper presented case studies which offered more details on implementation including the processes, operations and such gains as experienced from employing IoT and AI. It is evident from the analysis that IoT enables detailed data acquisition and real-time observation and monitoring of devices, whereas AI optimises predictive modelling and decision-making. These result to imperative upgrades in energy efficiency, cuts in operation costs and strengthening in system performance. This paper provides an insight into the subject of smart EMS’ application of IoT and AI in the context of energy management and presents recommendations to the practitioners and scholars. The implications also brought out future research directions in terms of improving the AI models and extending the use of IoT across a range of energy management scenarios.