# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 3 Overview of AI Technology
### Application of AI in Architectural Design
#### 3.2.4 Optimize Building Energy Consumption  
Energy optimization is a crucial part of building projects, covering strategies ranging from efficient design, intelligent system selection, and operational practices to user behavior modification and renewable energy integration. Central to this process are modern energy-efficient design principles, considering factors such as building orientation, window to wall ratio, insulation of the envelope, efficient HVAC systems, and intelligent control systems connected to the Internet of Things (IoT). According to the International Energy Agency's (IEA) Efficient World Scenario (EWS) and predictions in Energy Efficiency 2019, the building industry is expected to be 40% more energy-efficient by 2040 than in 2017, driven by advancements in technology and policy measures. Specifically, building space heating, water heating, cooking, and lighting will see improved efficiency, while space cooling and appliances will shift from negative growth in 2017 to positive growth by 2040 [9].  
To identify optimal solutions, researchers often use software such as DesignBuilder and EnergyPlus for energy simulations, generating and filtering multiple design combinations. For example, [PERSON] et al. [49] applied dynamic energy simulation software to optimize residential building energy performance. However, these methods often require long computations and highly detailed information models.  
With AI advancements, researchers began to use evolutionary optimization algorithms, such as GA and the Particle Swarm Optimization (PSO) algorithm, to optimize building envelope parameters. [PERSON] et al. [50] combined GA and EnergyPlus for residential building optimization. [PERSON], [PERSON] et al. [51] combined an ANN model with GA to optimize classical residential design.  
Multi-objective evolutionary optimization techniques have been applied to optimize building enclosures. [PERSON] et al. [52] explored energy use and lifecycle environmental impact, and [PERSON] et al. [53] utilized NSGAII optimization algorithms to optimize various building elements, such as walls, roofs, floors, and HVAC systems. These studies highlight the potential of evolutionary and intelligent algorithms to address multiple goals and constraints, achieving energy-efficient and cost-effective solutions.  
However, such studies often depend on tools, such as Transient System Simulation Tool (TRNSYS) and EnergyPlus, to run repeated energy simulations. These simulations are time-consuming and require detailed parameter settings, which can be impractical during early-stage design adjustments. The integration of multiple repeated simulations into the optimization process adds complexity, especially when managing variable parameters and optimization goals.  
Recently, [PERSON] et al. [54] proposed an integrated optimization approach that combines energy simulation, ANN, and GA to optimize sustainable building design. The results of the study provide valuable information for reducing energy consumption in residential buildings; it focused solely on energy goals, omitted cost considerations, and relied on a single optimization algorithm, without exploring other advanced machine learning algorithms that might improve prediction accuracy.