# 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
[125] used multiple machine learning methods for data-driven modeling of building energy systems, enabling prediction, potential analysis, and optimization of control strategies. [PERSON] et al. [122] developed a building energy performance model based on interpretable machine learning, which helps to understand the reasoning mechanism of the predictive model and balance model complexity and interpretability through the evaluation of \"trust\". [PERSON] et al. [174] used reinforcement learning and deep Q-learning to optimize the control of HVAC systems, improving energy efficiency and thermal comfort. [PERSON]'s team [186] presented a data-driven predictive control approach that uses time-series prediction and reinforcement learning to optimize HVAC operations in IoT smart buildings, resulting in energy savings and improved thermal comfort. [PERSON] and [PERSON] [109] used the MOB algorithm and recursive partition method to study the impact of energy variables on energy consumption, revealing the correlation between building energy consumption and specific design and system operation. Thisprovides an essential basis for the energy-saving design of American office buildings. At the same time, [PERSON]'s team [164] used the YOLOv4 algorithm and digital twin technology, combined with environmental perception and keyframe similarity mechanism, to achieve a significant improvement in the energy efficiency of indoor lighting, reducing operation and maintenance time and costs. In addition, [PERSON]'s team [157] used ten machine learning algorithms to deeply explore the energy characteristics of urban buildings through model adjustment, variable importance analysis, and local spatial analysis. For calibration and evaluation of energy models, [PERSON]'s team [115] proposed a new algorithm for optimizing energy loss through semi-supervised machine learning and clustering algorithms for building energy modelling. The team also developed a website to visualize the automated SSEM model. [PERSON] et al. [143] combined approximate Bayesian computation and machine learning for fast and reliable calibration of building energy models created with the EnergyPlus program. [PERSON] et al. [154] used extreme learning machine (ELM), multiple linear regression, support vector regression, and other methods to develop prediction models. They proposed a prediction time step strategy based on building thermal response time. Feature clusters perform best. [PERSON] et al. [162] use digital twins and AI to improve the cyber-physical building energy management system and develop an intelligent energy grid management system. The results show that DT-based real-time monitoring can reduce the gap between energy performance and actual building performance. [PERSON]'s team used algorithms such as decision trees, support vector machines, and digital twins to optimize real-time energy pricing and provide more specific insights into efficiency. [PERSON] et al. [166] combined digital twin technology and deep neural network to improve the efficiency of multi-vector and integrated energy systems and reduce operating costs. Research by [PERSON] et al. [167] emphasizes the value of digital twins in planning, design, and operation optimization. The research of [PERSON]'s team [185] aims to improve the comfort of building users through digital twin technology, combined with probabilistic models and provides adequate support for decision making.