Imputing missing indoor air quality data with inverse mapping generative adversarial network

Zejun Wu

doi:10.1016/j.buildenv.2022.108896

2022-05-01

PRE

Zejun Wu

Chao Ma

石

石小川 (Xiaochuan Shi) *

Libing Wu

Yi Dong

Miloš Stojmenović

DOI：10.1016/j.buildenv.2022.108896

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摘要

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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.

Keyword:

Indoor air quality

Missing data imputation

Generative adversarial network

Denoising auto-encoder

Bi-directional recurrent neural network