Powering Efficiency: Exploring Artificial Intelligence for Real-time Energy Management in Buildings

Abstract

This paper emphasizes the need for efficient energy consumption in buildings due to the increase in pollution and economic growth, which has led to an increase in energy consumption worldwide. Therefore, a real-time energy management system is needed to overcome the deficiency of energy consumption and improve energy efficiency. Further to adopt modern architecture, we introduce artificial intelligence; to identify the factors involved in optimizing energy consumption in buildings is an important factor. The paper also suggests different machine learning (ML) based algorithms for data cleaning, processing, and analysis. This includes different studies that used AI-based techniques for real-time energy management systems, including reinforcement learning, rule-based approach, and mixed integer linear programming, to reduce energy consumption by 20%-30%. The use of AI in energy management in buildings holds great potential for world’s scientific community. This technology enables data-driven decision-making, fosters energy conservation, and promotes advancements in the field of energy science, contributing to a greener and more sustainable future. The paper concludes that the use of AI-based approaches for energy-efficient systems can predict future energy demands by using previous data and building characteristics, making it more efficient than previous monitoring and alert systems.