Internet of things and machine learning applied to the thermal comfort of internal environments

Nascimento, Matheus; Lopes, Paulo Batista

doi:10.1177/1420326x221097338

Cited by 5 publications

(2 citation statements)

References 25 publications

(66 reference statements)

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“…Such a graph learning framework could standardize and unify the various query languages of different graph databases (e.g., Cypher and GSQL), integrate state-of-the-art deep learning frameworks like PyTorch ( 192 ), TensorFlow ( 193 ), Deeplearning4j ( 194 ), Microsoft CNTK ( 195 ), or flux ( 196 ), and allow clear visualizations. A framework using relational databases already exists ( 197 ) and showed the potential for easy application of machine learning algorithms and visualizations of the results ( 198 ).…”

Section: Discussionmentioning

confidence: 99%

The importance of graph databases and graph learning for clinical applications

et al. 2023

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The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

The importance of graph databases and graph learning for clinical applications

et al. 2023

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“…In recent years, the rise of machine learning 25 has set off an upsurge in applying it in the fields of building energy consumption, ventilation and thermal comfort prediction and evaluation. [26][27][28] Machine learning has the potential for rapid prediction of indoor environments. 29 In thermal comfort, the most widespread application is the data-driven building of models for predicting personal thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

Using random forests to predict passengers’ thermal comfort in underground train carriages

Huang

et al. 2022

Indoor and Built Environment

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This research developed an intelligent ensemble machine learning prediction model for the thermal comfort of passengers inside the compartment of the subway. Data sources used for data-driven modelling were obtained from on-site measurements and passengers’ questionnaires in the compartments of the Nanjing subway. The four models were established using methodologies of Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT) in machine learning, respectively. The performance of the RF method was compared with DT, LR and SVM in terms of conventional statistical metrics, namely, Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Correlation Coefficients squares (R2). Thermal Sensation Vote with the seven-level indicator (TSV-7) and Thermal Sensation Vote with the three-level indicator (TSV-3) were employed to obtain passengers’ thermal comfort and evaluate the models’ predictions. In this study, the R2 value of the RF model is 0.6527 and 0.6607 for TSV-7 and TSV-3, which shows higher accuracy than DT, LR and SVM models in predicting the two kinds of Thermal Sensation Vote (TSV). The results show that the predictive performance of the proposed RF model is outstanding, and it can predict the TSV value of passengers inside the compartment of the subway more efficiently.

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Studying occupant’s heat exposure and thermal comfort in the kitchen through full-scale experiments and CFD simulations

Luo

Guo

Feng

et al. 2022

Indoor and Built Environment

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This study investigated kitchen heat exposure and thermal comfort through full-scale experiments and computational fluid dynamics (CFD) simulations. Experiments were conducted in a kitchen-style climate chamber with three stove conditions, three exhaust flowrates of range hood, three external conditions and three makeup air sources. CFD simulations were conducted accordingly to explore more nuances aspects of kitchen thermal environment. The experimental results showed that as the existence of a high-temperature stove, the air temperatures around the cooking staff, the net thermal radiations and local skin temperatures at exposed body parts increased significantly during the cooking period. External conditions and the makeup air source can significantly affect thermal distributions across the kitchen space and the heat exposures around the cooking staff. By adding partitions, using induction cooktops, and turning on the kitchen air conditioner can help to reduce cooking staff’s heat exposure from the gas-burning stove. The CFD simulations were consistent with the measured experimental results. The heat exposure in the cooking zone is shown to be dominated by horizontal thermal asymmetry. The occupants’ thermal comfort in the kitchen can be improved if the makeup air source came from the cooler side with a higher flowrate of range hood.

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Internet of things and machine learning applied to the thermal comfort of internal environments

Cited by 5 publications

References 25 publications

The importance of graph databases and graph learning for clinical applications

The importance of graph databases and graph learning for clinical applications

Using random forests to predict passengers’ thermal comfort in underground train carriages

Studying occupant’s heat exposure and thermal comfort in the kitchen through full-scale experiments and CFD simulations

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