Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder
https://orcid.org/0000-0003-2239-5982
Abstract The proportion of buildings occupying underground space has increased with three-dimensional urban development. Thermal comfort is crucial to the design of underground spaces and plays an important role in the optimization of building environment controls. Owing to limitations in recording various practical environmental parameters, it is difficult to access large data and further to establish an accurate forecasting model for the thermal comfort of an underground space. This paper addresses the problem from the perspective of data enhancement. A model for generating underground space data based on a variational autoencoder is proposed. The model maps data of the thermal comfort of an underground space to a highly compressed latent layer space and generates data in an unsupervised manner. The forecasting models were trained using the generated data, resulting in accuracy improvements of 41.34%–45.31%. Hence, the proposed generative model can learn effective real data features. The results also demonstrate that the adjustment of ventilation is more effective than the adjustment of the temperature and relative humidity in improving the thermal comfort of an underground space. The findings of this research will provide better thermal comfort evaluation for the operational management of building environment in underground spaces.
Highlights A thermal comfort data generative model expands limited real data sets. R-squared of a forecasting model based on generated data is substantially higher. The generated forecasting models have a lower risk of overfitting. Air temperature and its difference between adjacent spaces are equally important. The adjustment of ventilation is a more effective means to improve thermal comfort.
Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder
https://orcid.org/0000-0003-2239-5982
Abstract The proportion of buildings occupying underground space has increased with three-dimensional urban development. Thermal comfort is crucial to the design of underground spaces and plays an important role in the optimization of building environment controls. Owing to limitations in recording various practical environmental parameters, it is difficult to access large data and further to establish an accurate forecasting model for the thermal comfort of an underground space. This paper addresses the problem from the perspective of data enhancement. A model for generating underground space data based on a variational autoencoder is proposed. The model maps data of the thermal comfort of an underground space to a highly compressed latent layer space and generates data in an unsupervised manner. The forecasting models were trained using the generated data, resulting in accuracy improvements of 41.34%–45.31%. Hence, the proposed generative model can learn effective real data features. The results also demonstrate that the adjustment of ventilation is more effective than the adjustment of the temperature and relative humidity in improving the thermal comfort of an underground space. The findings of this research will provide better thermal comfort evaluation for the operational management of building environment in underground spaces.
Highlights A thermal comfort data generative model expands limited real data sets. R-squared of a forecasting model based on generated data is substantially higher. The generated forecasting models have a lower risk of overfitting. Air temperature and its difference between adjacent spaces are equally important. The adjustment of ventilation is a more effective means to improve thermal comfort.
Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder
https://orcid.org/0000-0003-2239-5982
Abstract The proportion of buildings occupying underground space has increased with three-dimensional urban development. Thermal comfort is crucial to the design of underground spaces and plays an important role in the optimization of building environment controls. Owing to limitations in recording various practical environmental parameters, it is difficult to access large data and further to establish an accurate forecasting model for the thermal comfort of an underground space. This paper addresses the problem from the perspective of data enhancement. A model for generating underground space data based on a variational autoencoder is proposed. The model maps data of the thermal comfort of an underground space to a highly compressed latent layer space and generates data in an unsupervised manner. The forecasting models were trained using the generated data, resulting in accuracy improvements of 41.34%–45.31%. Hence, the proposed generative model can learn effective real data features. The results also demonstrate that the adjustment of ventilation is more effective than the adjustment of the temperature and relative humidity in improving the thermal comfort of an underground space. The findings of this research will provide better thermal comfort evaluation for the operational management of building environment in underground spaces.
Highlights A thermal comfort data generative model expands limited real data sets. R-squared of a forecasting model based on generated data is substantially higher. The generated forecasting models have a lower risk of overfitting. Air temperature and its difference between adjacent spaces are equally important. The adjustment of ventilation is a more effective means to improve thermal comfort.
Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder
Qiao, Renlu (Autor:in) / Li, Xiangyu (Autor:in) / Gao, Shuo (Autor:in) / Ma, Xiwen (Autor:in)
Building and Environment ; 207
19.10.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Structural Dynamics Feature Learning Using a Supervised Variational Autoencoder
| ASCE | 2025
Experimental evaluation of thermal comfort on underground trains
| British Library Conference Proceedings | 2005
An attempt to generate new bridge types from latent space of variational autoencoder
| ArXiv | 2023
Optimization of force-field potential parameters using conditional variational autoencoder
| DOAJ | 2023
Generative design of RHS/SHS profiles using Conditional Variational Autoencoder (CVAE)
| TIBKAT | 2024