# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 5 Digital Twin Technology for Energy Sector Development
### Algorithms and Deep Learning for Energy Efficiency
[139] used random forest and extreme gradient boosting models to successfully predict commercial buildings' annual energy use intensity (EUI) in the United States. In terms of residential buildings, on the one hand, [PERSON] et al. [152] used the XGBoost model and public data to predict the air-conditioning energy consumption of residential buildings in three different climate zones in the United States. The results showed that the model performed well (the overall R2 value was about 97% and the cross-validated R2 value 92%) and can help homeowners understand building performance and support energy efficiency decisions. On the other hand, [PERSON] et al. [149] quantified the impact of driving factors on residential buildings in the United States by combining machine learning and Monte Carlo methods, revealing that the relationship between total energy end use and total building area, number of rooms, number of windows, and indoor heating temperature setting Correlations of factors such as location, age of residents, and complex nonlinear effects of heating and cooling degree days also found that harsh climates may offset the effect of insulation level. For public buildings, [PERSON] [153] used six machine learning algorithms and recursive feature elimination to predict and analyze the importance of energy consumption features of public buildings. He found that the contribution rate of the first ten features exceeded 80%. When the number of features continued to increase, the marginal utility decreased significantly, highlighting data sets critical impact on model performance. At the same time, [PERSON] [180] used six meta-modelling techniques, including RDPG, A3C, LSTM, CNN, ANN and SVR, to predict the hour-level multi-performance vector of the gymnasium and found that the RDPG model prediction is the most accurate. The LSTM has high efficiency, which is suitable for emphasizing time and accuracy users, while the ANN model is easy to use and robust, suitable for time-limited users. In terms of early building energy consumption prediction, [PERSON]'s team [148] collected training data through incremental and enrichment methods. They found that enrichment methods are better at filling the design space and reducing generalization errors while emphasizing the analysis of sample distribution to improve the importance of generalization ability. On the other hand, [PERSON] [158] used machine learning and EnergyPlus simulations to estimate the energy use intensity of bank branches and found that lighting power density and weather variables are vital factors, and the support vector machine (SVM) model has the highest accuracy, which can be applied to future benchmark tests and other types of buildings. Machine learning techniques are also widely used to optimize prediction accuracy for building cooling and heating load simulation. [PERSON] et al. [176] used microclimate data and machine learning to predict cooling load. They found that microclimate variables such as average air temperature and absolute humidity had a significant impact. At the same time, [PERSON] and [PERSON] and [PERSON] et al. [113; 136] used statistical machine learning and multiple machine learning methods to quantify residential buildings' energy performance and predict office building cooling load, and both achieved minor prediction errors. Moreover, the artificial neural network model with a bagging ensemble outperformed other models in prediction accuracy. In addition, [PERSON] [156] introduced a machine-learning model based on dynamic feedback, which significantly reduces the computational load and maintains good energy and thermal comfort performance. In addition, [PERSON] et al. [132] used JAVA programming and parameter optimization strategies to predict and optimize the application of building energy efficiency with high computational efficiency. [PERSON] [168] adopted Broad Learning System (BLS) technology for building modelling and energy analysis.