Issue |
E3S Web Conf.
Volume 356, 2022
The 16th ROOMVENT Conference (ROOMVENT 2022)
|
|
---|---|---|
Article Number | 04028 | |
Number of page(s) | 4 | |
Section | Airflow Visualization, Measurement and Simulation | |
DOI | https://doi.org/10.1051/e3sconf/202235604028 | |
Published online | 31 August 2022 |
Predicting Unsteady Indoor Temperature Distributions by POD-DNN
1 Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
2 I.I.S., The University of Tokyo, Tokyo, Japan.
* Corresponding author: c-wei@iis.u-tokyo.ac.jp
In this study, to predict unsteady temperature distributions, POD-DNN was utilized, where DNN was trained to predicted coefficients of POMs. Two strategies, flatten POD-DNN and nested POD-DNN were compared. The flatten POD-DNN provided high accuracy if training data is sufficient, but otherwise very inaccurate. The nested POD-DNN roughly predicted the development of temperature fields even training data was small. The results showed their different sensitivities to the training data size.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.