Rapid monitoring of indoor air quality for efficient HVAC systems using fully convolutional network deep learning model

Shin, Sang‐Hun; Baek, Keuntae; So, Hongyun

doi:10.1016/j.buildenv.2023.110191

Cited by 22 publications

(10 citation statements)

References 47 publications

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“…In addition, ML models can be used to derive a nonlinear relationship between physically coupled input and output features. Therefore, effective ML models, such as multiple linear regression (MLR), [43,44] k-nearest neighbors (KNNs), [41,45,46] deep neural networks (DNNs), [47][48][49] and other neural networks [37,39,42,50] based on sufficient and reliable datasets have been widely investigated to construct precise prediction models. Furthermore, once trained, an accurate sensor model can estimate the desired properties or variables in real time.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, once trained, an accurate sensor model can estimate the desired properties or variables in real time. [39,50] In this study, a novel method for flow-sensing systems is demonstrated by integrating the advantages of 3D printing technologies and ML algorithms. Figure 1 shows the overall device concept of an ML-based flow sensor (MFS) used to predict the airflow rate by applying the principle of hot-film anemometers.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, once trained, an accurate sensor model can estimate the desired properties or variables in real time. [ 39,50 ]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Shin,

Baek,

Choi

et al. 2024

Advanced Intelligent Systems

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This article presents a novel airflow rate sensing method based on the principle of a hot‐film flow sensing device with data‐driven machine learning (ML) models. In addition, to combine the two signals of the sensor (the resistance changes of the heaters) and predict the output (airflow rate), three different ML multivariate regression models, i.e., multiple linear regression (MLR), k‐nearest neighbor (KNN), and deep neural network (DNN) models, are trained and compared using 8400 experimentally obtained data. Using sensor fusion techniques, the average mean absolute error (MAE) and mean squared error (MSE) of the KNN model are determined to be 0.01522 and 0.00132, respectively, in the range of 0–5.07 standard liters per minute. Compared with the average results obtained using only a single input, those obtained using a dual input indicate a significant decrease in the MAE and MSE by 85.69% and 96.68%, respectively. Furthermore, a transient analysis of the ML‐based flow sensor is conducted to investigate the response time and transient characteristics of the MLR, KNN, and DNN models. The results of this study contribute to the advancement of airflow management systems for various industrial applications, such as building ventilation, gas leakage detection, and energy systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Shin,

Baek,

Choi

et al. 2024

Advanced Intelligent Systems

Self Cite

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“…Due to the vast number of variables and nonlinearity involved, machine-learning-based algorithms are preferred for modeling. Fuzzy logic [ 16 ] and artificial neural networks (ANNs) [ 17 ] have been explored for thermal comfort control, while deep learning methods such as convolutional networks [ 18 ] and long short-term memory (LSTM) networks [ 19 ] have been used for air quality prediction. Another supervised learning method, support vector machine (SVM), has also gained popularity.…”

Section: Introductionmentioning

confidence: 99%

Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building

Peng

Hsieh

2023

Sensors

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Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants’ behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants’ thermal comfort level with reasonable computing time. In addition, this model can improve occupants’ thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings.

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“…Deep learning architectures specialized to analyze sequential data have been recently developed even for flow simulations of air quality monitoring, mostly to forecast particle concentrations (Navares and Aznarte 2020;Esager and Ünlü 2023) and for temperature control (Jung et al 2022). Some of these simulations are powered by pure deep learning models, while others are also making use of CFD simulations (Shin et al 2023), mostly for the dataset generation for the training of the model. Furthermore, while some deep learning applications are purely data driven built, some others feature physically constrained deep learning architectures (Raissi et al 2019;Mohan et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

Calzolari,

Liu

2023

Build. Simul.

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This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics (CFD) simulations and deep learning models. The coupling approach is showcased by the development of a data-driven turbulence model. The new turbulence model is built using a deep learning neural network, whose mapping structure is based on a zero-equation turbulence model for built environment simulations, and is coupled with the CFD software OpenFOAM to create a hybrid framework. The neural network is a standard shallow multi-layer perceptron. The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm. The framework is trained on an indoor environment case study, as well as tested on an indoor office simulation and an outdoor building array simulation. Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes (RANS) turbulence models, while maintaining a similar level of accuracy. The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios. Despite some challenges, namely the mapping constraint, the limited training dataset size and the source of generation of training data, the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

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Rapid monitoring of indoor air quality for efficient HVAC systems using fully convolutional network deep learning model

Cited by 22 publications

References 47 publications

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building

Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

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