A Review on AI-Driven Energy Consumption Forecasting for Smart Buildings

초록

Smart buildings integrate advanced technologies with user-friendly interfaces to optimise energy consumption while enhancing occupant comfort. Achieving sustainable living in smart homes relies on motion sensors, infrared sensors, and other intelligent systems that automate and regulate energy usage. While these technologies improve user experience, reducing energy consumption remains a critical challenge for stakeholders committed to sustainability. This paper presents different AI methods for energy consumption forecasting. This review also provides a comparative analysis of various AI-driven, deep learning and machine learning approaches, evaluating their effectiveness in forecasting energy demand and promoting sustainability in smart buildings. This review also gives an overview of predictions on the amount of energy smart buildings will use to achieve the goal of making them more energy-efficient The study also looks at different ways to predict the future for both residential and non-residential buildings. © 2026 University of Modern Sciences. IET Smart Grid published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

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