| Issue |
E3S Web of Conf.
Volume 396, 2023
The 11th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC2023)
|
|
|---|---|---|
| Article Number | 01044 | |
| Number of page(s) | 7 | |
| Section | Indoor Environmental Quality (IEQ), Human Health, Comfort and Productivity | |
| DOI | https://doi.org/10.1051/e3sconf/202339601044 | |
| Published online | 16 June 2023 | |
Image Generation Technique of Indoor Temperature Distribution Using Transposed Convolutional Neural Network
1 DAI-DAN CO., LTD. (Japan)
2 Institute of Industrial Science, The University of Tokyo (Japan)
* Corresponding author: ikedahiroki@daidan.co.jp
It is important to maintain a comfortable indoor thermal environment because people spend most of their time in buildings. Computational fluid dynamics (CFD) applications can not only simulate the indoor environment under various boundary conditions but also visualize analysis results as contour plots to help humans understand them easily. However, calculations are required for each boundary condition, and a significant amount of time is required to obtain the results. In recent years, artificial intelligence has been utilized in various fields, including image processing technology. Generating temperature distribution images using artificial intelligence instead of CFD analysis is expected to reduce the number of CFD cases and time required for preliminary studies. In this study, a transposed convolutional neural network was used to generate a model for the fast prediction of indoor temperature distribution, and case studies were conducted using a combination of training data. Consequently, it was confirmed that by appropriately selecting 108 out of 300 data points arranged in a grid, a model that predicts temperature distribution with generally good accuracy was generated for the untrained data.
© The Authors, published by EDP Sciences, 2023
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