# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 3 Overview of AI Technology
### Application of AI in Architectural Design
#### 3.2.3. Building Energy Consumption Forecast
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Position** & **AI Technology Used** & **Conclusions** \\ \hline \multirow{6}{*}{[PERSON], [PERSON] et al. [43]} & \multirow{6}{*}{Britain} & Gradient Boosting (GB) algorithm & By comparing various machine learning algorithms, the study reveals GB as the most accurate for building energy performance prediction. Feature selection and hyperparameter tuning impact model performance, and while GB excels in accuracy, each algorithm has unique strengths depending on the scenario, offering valuable insights for energy evaluation in building design. \\ \cline{1-1}  & & & Propose a component-based machine learning method to predict building energy performance, validated through testing. It simplifies modeling, provides inter-component information, and expands the design space, but is limited by the range of training data. Future work will explore the link between model effectiveness and training data to handle complex designs. \\ \cline{1-1}  & & & Propose a Deep Reinforcement Learning (DRL)-based multi-objective optimization method for green building design, demonstrating the Deep Deterministic Policy Gradient (DDPG) model’s superiority over traditional algorithms in optimization rate, strategy stability, and generalization. Future work will improve the evaluation system to enhance its practicability and address ethical considerations. \\ \cline{1-1}  & & & Focus on comparing ANN and RF for predicting hotel HVAC (Heating, Ventilation, and Air Conditioning) energy consumption, finding that ANN is more accurate, while RF has shorter training time and better handling of missing values. Future studies should explore additional algorithms and factors to improve prediction accuracy and energy management. \\ \cline{1-1}  & & & Focus on predicting energy consumption in U.S. commercial buildings using multiple algorithms. SVM and RF excel in Total Energy Use Intensity (EUI) prediction, with linear regression showing advantages in some cases. Future work should address performance variations across energy subsystems. \\ \cline{1-1}  & & & Focus on using RF to predict hourly building energy consumption, showing superior accuracy and variable sensitivity compared to RT and SVR. It reveals energy factor changes across semesters, offering a new approach for building energy management. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._use in office buildings and net energy consumption in Turkey. However, ANNs have limitations in adapting to changes in different building components or systems. In addition to ANNs, SVMs are also considered a powerful data mining technique [42; 43]. The application of SVMs in predicting building energy consumption shows good prediction results, though optimizing their parameters remains a challenge. With advances in computer configuration, the application of deep learning in predicting building energy consumption is also increasing [44]. DNNs significantly improve the accuracy of energy consumption prediction by learning building characteristics [45]. In addition, traditional linear regression (LR) models, RF [46; 47; 48], and gradient-boosting models such as XGBoost have all been shown to be effective in predicting the heating and cooling loads of buildings. More recently, machine learning models based on gene expression have also been developed to predict building shear strength, offering improved accuracy by capturing complex variable relationships, albeit with greater implementation complexity.