文章基本信息

标题：Predicting Unsteady Indoor Temperature Distributions by POD-DNN
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作者：Chenghao Wei ; Ryozo Ooka ; Bingchao Zhang 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2022
卷号：356
页码：1-4
DOI：10.1051/e3sconf/202235604028
语种：English
出版社：EDP Sciences
摘要：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.