# 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
This model has higher prediction accuracy and fast real-time update capabilities compared to traditional models. The Build2 Vec spatial model developed by [PERSON] et al. [169], combined with building information modelling (BIM) and other data, has also improved prediction accuracy. In building energy consumption prediction, [PERSON] and [PERSON] [150] found that four algorithms (CART, EBT, ANN, and DNN) performed well, among which the DNN model with four hidden layers was the best. Research by [PERSON]'s team [127] shows that ran dom forest models best predict heating loads in energy-efficient buildings. [PERSON] et al. [154] compared 15 different machine learning models and found that the Gaussian process regression model and support vector machine performed well in prediction accuracy, model stability, and calculation speed. [PERSON] and [PERSON] [155] proposed a method of mixing meta-heuristics and machine learning, which can optimize the daily operation plan of building energy systems and reduce daily operating costs by more than 13.4%. Studies by [PERSON] et al. [121] and [PERSON] et al. [127] have shown that machine learning is effective. [PERSON] et al. used various machine learning methods, especially the extreme learning machine model performed best under feature set 4, and the optimal number of nodes was 11. On the other hand, [PERSON] et al. [127] found that wall area, total height, orientation, and glaze area are the main features affecting building energy consumption, and the prediction performance of RF, lazy K-star, RDT, and AMT models was excellent. Furthermore, feature reduction can improve model performance, and the most accurate MLP models provide an optimized structure for predicting HL and CL variables. [PERSON] et al. and [PERSON] et al. [96,181] predicted the energy consumption of air conditioners in buildings by using a variety of machine learning algorithms, such as SVM, RF, MLP, DNN, RNN, LSTM, and GRU, and found that the performance of RF and SVM particularly prominent. Research by [PERSON] and [PERSON] [120] demonstrates that machine learning predictions of fuel consumption in commercial buildings are also feasible in different climates and building types. [PERSON] et al. [179] used surrogate models and MPC methods to optimize the energy retrofit for specific buildings and realize the maximum passive operation of the HVAC system. In the application of digital twin technology, studies by [PERSON], [PERSON], [PERSON], and [PERSON] [134,160,163,185] have shown that digital twin technology can significantly improve the energy efficiency of buildings through building modelling, energy analysis, and multi-objective optimization and efficiency and reduce energy consumption. For other specific energy consumption predictions, [PERSON] et al. [135] used general circulation models to assess the impact of climate change on building energy performance. [PERSON] and [PERSON] [119] developed a Markov model based on change point analysis to improve occupancy prediction accuracy for commercial buildings. [PERSON] and [PERSON] [177] used artificial neural networks and adaptive neuro-fuzzy inference system models to significantly improve thermal comfort and save energy in educational buildings in tropical climates. [PERSON] et al. [161] conducted architectural modelling and energy analysis on Chilean houses, which were successfully applied in different climate regions. Using data-driven methods and digital twin technology, [PERSON] et al. [184] enabled real-time data collection and analysis to improve resident comfort and ensure sustainable operations. In energy efficiency analysis and improvement, [PERSON] et al. [178] estimates the human metabolic rate by depth vision sensor and RNN to adjust the set point of HVAC equipment in real-time to achieve effective energy saving. [PERSON] et al. [116] combine data-driven machine learning models and physics-based energy simulations to predict and understand the impact of urban environments on energy consumption more accurately. [PERSON] et al.