Review of emerging smart techniques and their role in enhancing energy efficiency in residential and commercial buildings

Abstract

This review explores emerging smart technics in enhancing energy efficiency in commercial and residential buildings using systematic and bibliometric approaches from 1990 to 2024. According to the findings, the increase in internet of things applications, like AI and especially machine-learning applications, has enabled smarter buildings in recent years. The advancements of modern data analytics, predictive modelling, and real-time monitoring create a fair base for advancing into new paradigms for energy management. Energy yield prediction and building performance enhancements are ensured through machine-learning techniques, such as ensemble learning, neural networks, and support vector regression. The study found that deep reinforcement learning and fuzzy logic constitute those technologies that automate the consumption behaviors while perfectly balancing efficiency and comfort of occupants. According to the results, smart technologies offer better options toward energy efficiency but encounter major hurdles like poor internet availability, social acceptance, regulatory issues, high upfront cost, scaling issues, and data privacy. Real-time data coupled with smart technology systems should be combined to develop hybrid machine-learning models and predictive energy consumption models. For the attainment of energy efficiency goals, standardization of energy-efficient buildings and greening people's energy practices are key.