Title : A bibliometric review on artificial intelligence for smart buildings

Objectives
The goal of this paper is to provide a bibliometric review of the research and published articles on artificial intelligence (AI) for smart buildings. It examines trends and patterns in this field through bibliometric data analysis and explores how AI research in smart buildings has evolved.

Strategies and Methods
Strategies: Use bibliometric methods to identify key articles, prominent authors, and highly cited journals in the field of AI for smart buildings. Analyze research trends over time and identify emerging and hot topics in this area.
Methods: Utilize bibliometric tools such as Scopus, Web of Science, and Google Scholar to collect article data, identify research patterns, and analyze the networks of connections between articles and authors.

Applications
Research Trends Analysis: Identify and analyze research trends in AI for smart buildings and predict future developments in this field.
Evaluation of Article and Author Impact: Assess prominent articles and authors and identify the most-cited papers in this domain.
Identification of Emerging Research Areas: Recognize and classify new AI areas expanding within smart buildings, such as deep learning, energy consumption optimization, and security.

Models and Algorithms
Network Analysis: Analyze collaboration networks among authors and research institutions using bibliometric tools to identify relationships and overlaps in scientific work.
Citation Models: Use citation models to identify highly cited papers and analyze their influence on shaping scientific knowledge in AI for smart buildings.
Research Trend Analysis: Simulate research trends over time and predict future research directions using bibliometric data.

Results
The results indicate that AI research for smart buildings has seen substantial growth in recent years. Key topics in this area include energy optimization, automated system control, and security in smart buildings. Additionally, certain authors and institutions have been particularly active in this field.

Challenges and Limitations
Data Limitations: A major challenge in using bibliometric methods is the limited access to complete and accurate article data.
Data Inconsistencies: Lack of uniformity in data formats and standards can complicate the analysis process.
Focus on Specific Articles: Some papers may not be accessible for analysis, or bibliometric results might be limited due to overemphasis on highly cited papers.

Conclusion
This bibliometric review shows that AI is a growing key technology in the advancement of smart buildings. Additionally, bibliometric analyses can provide valuable insights into research trends and scientific collaboration patterns in this field.
Future Work
Future research should focus on developing improved bibliometric methods to evaluate the impact of AI research in smart buildings. There is also a need for data collection and standardization to better analyze research trends and predict future developments in this domain.