Title : Review of artificial intelligence techniques in green/smart buildings

Objectives
The objective of this paper is to review artificial intelligence (AI) techniques in green and smart buildings. The paper explores various AI methods that can be used to enhance energy efficiency, reduce pollution, and increase productivity in these buildings.

Strategies and Methods
Strategies: Analyze and evaluate AI techniques for optimizing energy consumption, predicting energy usage patterns, and utilizing renewable energy sources in green and smart buildings.
Methods: Review of previous research and studies on AI applications in green and smart buildings, analyzing algorithms such as machine learning, optimization algorithms, and neural networks, and assessing their advantages and disadvantages in this field.

Applications
Energy Management: AI is used to optimize energy consumption in green buildings, including automatic adjustment of temperature, lighting, and ventilation.
Renewable Energy Utilization: AI techniques help optimize the use of renewable energy sources like solar panels and wind turbines in buildings.
Air Quality Control: AI is used to manage indoor air quality and predict ventilation needs based on environmental conditions.
Pollution Reduction: AI helps reduce pollution and carbon emissions in smart and green buildings by optimizing energy systems and better managing resources.

Models and Algorithms
Machine Learning Models: Algorithms such as regression, decision trees, and clustering are used to predict and optimize energy consumption.
Neural Networks: Neural networks are used to simulate and predict energy consumption patterns and analyze system performance data.
Optimization Models: Optimization models are applied for efficient allocation of energy resources and consumption reduction under specific conditions.
Evolutionary Algorithms: Evolutionary algorithms are used to optimize energy consumption processes and resource management.

Results
The use of AI in green and smart buildings can lead to significant reductions in energy consumption, costs, and improvements in productivity. Additionally, AI techniques can guide buildings towards greater sustainability and cost-effectiveness. AI also helps reduce emissions and improve indoor environmental conditions.

Challenges and Limitations
Insufficient and Inaccurate Data: A key challenge in AI applications is the need for accurate and complete data to train models and algorithms.
Implementation Complexity: Implementing AI systems in green and smart buildings may be complex and costly.
Resistance to Change: Adoption and implementation of these technologies may face resistance due to traditional practices.

Conclusion
This paper shows that AI can play a significant role in improving energy efficiency and sustainability in green and smart buildings. With recent advancements in AI techniques, these technologies can optimize energy consumption, improve the use of renewable resources, and reduce emissions.

Future Work
Future research is needed to enhance predictive models, optimize energy processes, and improve data accuracy. Furthermore, more attention should be given to the integration of various technologies for the seamless implementation of AI systems in buildings.