Imputing missing indoor air quality data with inverse mapping generative adversarial network
文献类型：期刊论文
| 作者 | Wu, Zejun3; Ma, Chao3; Shi, Xiaochuan3; Wu, Libing3; Dong, Yi1,4; Stojmenovic, Milos2 |
| 刊名 | BUILDING AND ENVIRONMENT
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 215期号:-页码:- |
| 关键词 | Indoor air quality Missing data imputation Generative adversarial network Denoising auto-encoder Bi-directional recurrent neural network |
| ISSN号 | 0360-1323 |
| 英文摘要 | Sensors deployed all over the buildings are nowadays collecting a large amount of data, such as the Indoor Air Quality (IAQ) data which can provide valuable suggestions on improving indoor environments and energy consumption strategies. However, as treated as Multivariate Time Series (MTS), IAQ data often contain missing values that severely limit further analysis on them. Unfortunately, most of the existing methods fail to handle a couple of technical issues due to the complexity of MTS data, such as data distribution approximation, removing the redundancy, and so on. In this paper, we formulate the IAQ missing data imputation problem and propose an Inverse Mapping Generative Adversarial Network (IM-GAN) to tackle that problem. IM-GAN takes advantage of Bi-directional Recurrent Neural Network (BRNN), Denoising Auto-Encoder (DAE), and Generative Adversarial Network (GAN) to overcome the aforementioned technical issues. To validate the effectiveness of our proposed IM-GAN, we conduct comprehensive experiments on two public IAQ datasets GAMS and Gainesville. Results show that our IM-GAN achieves the new state-of-the-art performance in accurately estimating missing values in indoor air quality time series data, with the average performance of 0.1566 and 0.0789 in terms of Mean Relative Error, and 17.2884 and 2.7434 in terms of Mean Absolute Error on GAMS and Gainesville respectively at different missing rates. Our ablation study and visualization also validate that IM-GAN effectively overcomes the aforementioned technical issues by capturing data distribution, eliminating network saturation, and so on for IAQ data imputation. |
| 学科主题 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000780338000003 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35157]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, China 2.Department of Computer Science and Electrical Engineering, Singidunum University, Belgrade, Serbia 3.School of Cyber Science and Engineering, Wuhan University, Wuhan, China 4.Laboratory of Geomechanics and Geotechnical Engineering, Wuhan, China |
| 推荐引用方式 GB/T 7714 | Wu, Zejun,Ma, Chao,Shi, Xiaochuan,et al. Imputing missing indoor air quality data with inverse mapping generative adversarial network[J]. BUILDING AND ENVIRONMENT,2022,215(-):-. |
| APA | Wu, Zejun,Ma, Chao,Shi, Xiaochuan,Wu, Libing,Dong, Yi,&Stojmenovic, Milos.(2022).Imputing missing indoor air quality data with inverse mapping generative adversarial network.BUILDING AND ENVIRONMENT,215(-),-. |
| MLA | Wu, Zejun,et al."Imputing missing indoor air quality data with inverse mapping generative adversarial network".BUILDING AND ENVIRONMENT 215.-(2022):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。