| Title | Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning |
| Authors | Zhao, Yuhong Wang, Naiqiang Liu, Zhansheng Mu, Enyi |
| Affiliation | Beijing Univ Technol, Coll Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China Beijing Univ Technol, Minist Educ, Key Lab Urban Secur & Disaster Engn, Beijing 100124, Peoples R China Peking Univ, Coll Urban & Environm Sci Urban & Econ Geog, Beijing 100871, Peoples R China |
| Keywords | ARTIFICIAL-NEURAL-NETWORK PREDICTION SIMULATION INTERNET SERVICE MODELS ANN |
| Issue Date | Feb-2022 |
| Publisher | BUILDINGS |
| Abstract | The operation and maintenance (O&M) of buildings plays an important role in ensuring that the buildings work normally, as well as reducing the damage caused by functional errors. There are obvious problems in the traditional O&M modality, and an effective way to solve them is to make the model smarter. In this paper, a digital twin framework for building operation is proposed, which consists of two key components: a digital twin O&M model and a machine learning algorithm. The process of establishing the digital twin model is introduced in detail, and the method is explained according to the structure, equipment, and energy consumption characteristics of the model. A mechanism of fusing the digital twin and machine learning algorithm is proposed and the prediction process based on an artificial neural network (ANN) is shown. Finally, based on a systematic summary of the modeling process and fusion mechanism, the development path and overall structure of the intelligent O&M system utilizing digital twins is proposed. |
| URI | http://hdl.handle.net/20.500.11897/638825 |
| DOI | 10.3390/buildings12020087 |
| Indexed | SCI(E) |
| Appears in Collections: | 城市与环境学院 |
