IoT-Based Smart Home Energy Management System (SHEMS) Using Networking and Automation

Physical energy meter reading concept is outdated and it is not efficient, leading to lots of wastage of manpower and errors, btaining accurate real-time readings is challenging and complex to achieve consistently. Current implementations of IoT technology allow real-time monitoring and energy management capabilities with the integration of IoT ecosystems, Digital Communication Technology networks, and Machine Learning algorithms to further analyze data as well as enhance user convenience. However, despite their promising potential, these systems face ongoing challenges, including adaptability, scalability, interoperability and enhanced cyber security resilience, particularly in large-scale deployments. This review examines present SHEMS and their capabilities, from strengths such as real-time tracking of energy usage, optimization and consumption of energy at offpeak hours by the application of predictive analytics using Machine Learning. This also elucidates areas for improvement, such as an improved integration of renewable energy technologies, simplification of the complexities surrounding large-scale deployment and sustainability over the long term. Addressing these deficits, this paper further promotes advancements in the design of SHEMS that are more secure, scalable and inter-operable, increasing communication across IoT ecosystems and by using smart grid technology, which could improve effective management of energy and a potential wider uptake. These suggestions are aimed at fine-tuning existing technologies to be easily integrated into modern, automated energy systems for better energy management.