Title : A state-of-the-art review on artificial intelligence for Smart Buildings

Objectives
The objective of this paper is to review the current state of artificial intelligence (AI) applications in smart buildings. It aims to identify and analyze recent advancements in this field and explore how AI is being utilized to improve efficiency, security, and comfort in smart buildings.

Strategies and Methods
Strategies: Analyze and evaluate current trends in the use of AI in smart buildings, examining various technologies and the challenges they face.
Methods: A comprehensive review and analysis of recent papers and research using statistical and analytical methods to identify best practices and innovations in the field. Additionally, categorize AI applications based on building types and specific needs.

Applications
Energy Management: AI helps optimize energy consumption in smart buildings by automatically adjusting heating, ventilation, and lighting systems.
Security and Surveillance: AI is used in security systems, including facial recognition and activity monitoring through smart cameras.
Control and Automation: Many building processes, such as temperature regulation, lighting, and electrical equipment, are automated using AI algorithms.
Simulation and Prediction: AI models are used to optimize system performance and predict potential issues before they occur.

Models and Algorithms
Machine Learning Models: AI algorithms such as supervised learning and deep learning are used to simulate and optimize building processes.
Neural Networks: Neural networks are employed to predict energy needs and enhance automation processes in buildings.
Optimization Models: AI optimization models are used to manage resources, reduce energy consumption, and improve overall efficiency in smart buildings.

Results
Studies have shown that AI applications in smart buildings can lead to significant improvements in efficiency, security, and resident comfort. Additionally, AI helps optimize energy systems, reducing energy consumption. These technologies also enhance user experience and improve the quality of life in smart buildings.

Challenges and Limitations
Complexity and High Implementation Costs: Implementing AI systems in smart buildings requires complex infrastructure and significant costs.
Incomplete or Incorrect Data: Inaccurate or missing data can negatively impact the performance of AI systems.
Privacy and Security: The use of smart systems requires data protection and safeguarding user privacy against cyber threats.

Conclusion
This paper demonstrates that AI has broad applications in smart buildings, significantly improving efficiency, security, and comfort. However, challenges such as implementation complexity, costs, and security issues still need to be addressed.

Future Work
Further research is needed to improve AI algorithms, optimize energy consumption, and enhance security and protective systems in smart buildings. Additionally, studies should focus on scaling AI systems for larger-scale applications, such as residential complexes.