The paper "AI-Driven Energy Management Systems for Smart Buildings" by Balakumar Muniandi et al. explores the transformative impact of Artificial Intelligence (AI) on energy management in smart buildings. The authors review the multifaceted functionalities, benefits, challenges, and future prospects of AI-driven energy management systems. These systems leverage advanced algorithms, machine learning, and data analytics to optimize energy usage, enhance operational efficiency, and advance sustainability goals. Key functionalities include predictive analytics for energy demand forecasting, adaptive control of HVAC systems, dynamic lighting management, and integration with renewable energy sources. The paper highlights the importance of AI in enabling smart buildings to participate in demand response programs, reducing operational costs, and contributing to grid stability and resilience. However, it also addresses challenges such as data privacy concerns, interoperability issues, and the need for skilled personnel. The authors emphasize the potential of AI-driven energy management systems to create smarter, more resilient, and sustainable built environments, while acknowledging the importance of addressing challenges through robust strategies and best practices.The paper "AI-Driven Energy Management Systems for Smart Buildings" by Balakumar Muniandi et al. explores the transformative impact of Artificial Intelligence (AI) on energy management in smart buildings. The authors review the multifaceted functionalities, benefits, challenges, and future prospects of AI-driven energy management systems. These systems leverage advanced algorithms, machine learning, and data analytics to optimize energy usage, enhance operational efficiency, and advance sustainability goals. Key functionalities include predictive analytics for energy demand forecasting, adaptive control of HVAC systems, dynamic lighting management, and integration with renewable energy sources. The paper highlights the importance of AI in enabling smart buildings to participate in demand response programs, reducing operational costs, and contributing to grid stability and resilience. However, it also addresses challenges such as data privacy concerns, interoperability issues, and the need for skilled personnel. The authors emphasize the potential of AI-driven energy management systems to create smarter, more resilient, and sustainable built environments, while acknowledging the importance of addressing challenges through robust strategies and best practices.