AI-Driven Energy Management Systems for Smart Buildings.

Abstract

The advent of Artificial Intelligence (AI) has revolutionized the energy management landscape for smart buildings, offering unparalleled opportunities for optimizing energy consumption, enhancing operational efficiency, and advancing sustainability goals. This paper provides a comprehensive review of AI-driven energy management systems tailored for smart buildings, exploring their multifaceted functionalities, benefits, challenges, and future prospects. [1],[4] By synthesizing existing literature and case studies, this research aims to elucidate the transformative potential of AI in reshaping the way energy is managed and utilized in the built environment. AI-driven energy management systems leverage advanced algorithms, machine learning techniques, and data analytics to intelligently monitor, analyze, and optimize energy usage within smart buildings. These systems integrate diverse components such as sensing devices, data preprocessing modules, optimization algorithms, and control systems to achieve optimal performance. Key functionalities include predictive analytics for energy demand forecasting, adaptive control of heating, ventilation, and air conditioning (HVAC) systems, dynamic lighting management based on occupancy patterns, and integration with renewable energy sources to enhance sustainability. AI enables smart buildings to participate in demand response programs, dynamically adjusting energy consumption in response to grid conditions and pricing signals. This flexibility not only reduces operational costs but also contributes to grid stability and resilience. However, the widespread adoption of AI-driven energy management systems faces several challenges, including data privacy concerns, interoperability issues, and the need for skilled personnel to operate and maintain these sophisticated systems.The paper underscores the importance of AI-driven energy management systems as transformative tools for optimizing energy utilization, improving building performance, and advancing sustainability objectives in the era of smart buildings. DOI: https://doi.org/10.52783/pst.280