Spectral band decomposition combined with nonlinear models: application to indoor formaldehyde concentration forecasting

Ouaret, Rachid; Ionescu, Anda; Petrehus, Viorel; Candau, Yves; Ramalho, Olivier

doi:10.1007/s00477-017-1510-0

Cited by 8 publications

(6 citation statements)

References 46 publications

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“…Formaldehyde concentration was forecasted using a non‐linear time series hybrid model based on spectral band decomposition coupled with a threshold autoregressive model . The hybrid model was used to forecast indoor formaldehyde concentrations.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.

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Section: Discussionmentioning

confidence: 99%

Machine learning and statistical models for predicting indoor air quality

et al. 2019

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“…A main problem with this approach was that it used the concentration from a previous time step to estimate the emission rate for the current time step because the current concentration is unknown. To improve the modeling accuracy for predicting time-resolved concentrations, we developed an approach to use hourly air exchange rate as a surrogate for the concentration in the bulk room air, which has been used in previous studies [ 14 , 38 , 39 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…Akyüz et al presented an implantation of artificial neural networks (ANN) for modeling the formaldehyde emission from particleboard based on manufacturing variables, including wood-glue moisture content, density of board, and pressing temperature [ 39 ]. Ouaret et al developed an approach using Fourier transform and two nonlinear model: threshold autoregressive (TAR) and Chaos dynamics models to forecast the formaldehyde concentration 12 h ahead in a regularly occupied office with diurnal pattern [ 40 ]. Zhang et al recently applied an artificial neural networks (ANN) approach to predict gas-phase VOC concentrations from four kinds of furniture in a chamber [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Zhao

Walker

Sohn

et al. 2022

IJERPH

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Recent studies have succeeded in relating emissions of various volatile organic compounds to material mass diffusion transfer using detailed empirical characteristics of each of the individual emitting materials. While significant, the resulting models are often scenario specific and/or require a host of individual component parameters to estimate emission rates. This study developed an approach to estimate aggregated emissions rates based on a wide number of field measurements. We used a multi-parameter regression model based on previous mass transfer models to predict formaldehyde emission rate for a whole dwelling using field-measured, time-resolved formaldehyde concentrations, air exchange rates, and indoor environmental parameters in 63 California single-family houses built between 2011 and 2017. The resulting model provides time-varying formaldehyde emission rates, normalized by floor area, for each study home, assuming a well-mixed mass balance transport model of the home, and a well-mixed layer transport model of indoor surfaces. The surface layer model asserts an equilibrium concentration within the surface layer of the emitted materials that is a function of temperature and RH; the dwelling ventilation rate serves as a surrogate for indoor concentration. We also developed a more generic emission model that is suitable for broad prediction of emission for a population of buildings. This model is also based on measurements aggregated from 27 homes from the same study. We showed that errors in predicting household formaldehyde concentrations using this approach were substantially less than those using a traditional constant emission rate model, despite requiring less unique building information.

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“…Feature extraction and parameter selection are key parts of artificial intelligence predictive models [15]. Ouaret et al [16] predicted indoor formaldehyde concentration by extracting features by spectral band decomposition. Yang et al [17] used empirical mode decomposition (EMD) to decompose the ammonia concentration data in pig houses at different scales, and extracted the local feature information of the ammonia concentration data series in pig houses, and the prediction accuracy and efficiency were improved.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Carbon Dioxide Concentration in Pig House Based on Deep Learning

Zang

Shuqin

et al. 2022

Atmosphere

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The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment.

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Spectral band decomposition combined with nonlinear models: application to indoor formaldehyde concentration forecasting

Cited by 8 publications

References 46 publications

Machine learning and statistical models for predicting indoor air quality

Machine learning and statistical models for predicting indoor air quality

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Prediction Model of Carbon Dioxide Concentration in Pig House Based on Deep Learning

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