Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology

Chaudhuri, Tanaya; Zhai, Deqing; Soh, Yeng Chai; Li, Hua; Xie, Lihua

doi:10.1016/j.enbuild.2018.02.035

Cited by 149 publications

(49 citation statements)

References 28 publications

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“…While each study that involved temperature collection described the positioning of the sensors for a skin temperature extraction [37][38][39] and how collected data were pre-processed, the majority of them did not provide biological/medical reasoning for the selected skin regions. Only Ghahramani et al [34] and Li et al [35] give proper background information with respect to the skin properties within different regions of the body and why a certain region was chosen.…”

Section: Resultsmentioning

confidence: 99%

“…The leading approach with respect to the amount of times used is the support vector machines (SVM), which appeared six times among studies [31,38,42,43,53,55]. Random forest and k-nearest neighbor (k-NN) appeared four times [31,35,39,53]. Additionally, ANN was used three times [29,43,56], just like linear regression [49,51,57].…”

Section: Resultsmentioning

confidence: 99%

“…There is a lack of explanation behind the selection of the population samples in the processed studies. Additionally, some studies [33,37,[39][40][41]48,57] discuss in detail the limitations posed by the number of participants and that the number of people should be increased in order to make their code/models perform better in real life applications. In addition, the sex of participants is not discussed in detail.…”

Section: Discussionmentioning

confidence: 99%

“…The review concludes that there is great potential for utilizing digital bio-sensing equipment within the given topic. Bio-metrical data can provide grounds for estimation of the thermal comfort at the workplace [30,39,56]. The facial skin temperature has proven to be a very useful parameter for the training of the machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

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A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

Marchenko¹,

Salaj²

2020

Applied Sciences

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Since 1997, scientists have been trying to utilize new non-invasive approaches for thermal discomfort detection, which promise to be more effective for comparing frameworks that need direct responses from users. Due to rapid technological development in the bio-metrical field, a systematic literature review to investigate the possibility of thermal discomfort detection at the work place by non-invasive means using bio-sensing technology was performed. Firstly, the problem intervention comparison outcome context (PICOC) framework was introduced in the study to identify the main points for meta-analysis and, in turn, to provide relevant keywords for the literature search. In total, 2776 studies were found and processed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. After filtering by defined criterion, 35 articles were obtained for detailed investigation with respect to facility types used in the experiment, amount of people for data collection and algorithms used for prediction of the thermal discomfort event. The given study concludes that there is potential for the creation of non-invasive thermal discomfort detection models via utilization of bio-sensing technologies, which will provide a better user interaction with the built environment, potentially decrease energy use and enable better productivity. There is definitely room for improvement within the field of non-invasive thermal discomfort detection, especially with respect to data collection, algorithm implementation and sample size, in order to have opportunities for the deployment of developed solutions in real life. Based on the literature review, the potential of novel technology is seen to utilize a more intelligent approach for performing non-invasive thermal discomfort prediction. The architecture of deep neural networks should be studied more due to the specifics of its hidden layers and its ability of hierarchical data extraction. This machine learning algorithm can provide a better model for thermal discomfort detection based on a data set with different types of bio-metrical variables.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

Marchenko¹,

Salaj²

2020

Applied Sciences

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“…The ability to mine indoor environmental data from measurement and occupant satisfaction/comfort data from surveys provides new opportunities for facility managers to more effectively respond to occupant complaints and optimize building performance. Both supervised and unsupervised learning methods have been adopted from the perspective of personal comfort learning [82]- [87]. Several elements are commonly considered within the scope of such studies, including indoor air quality (IAQ), indoor environmental (thermal, acoustic, visual, and spatial) quality, occupant health and safety, occupant comfort, and occupant complaints [88].…”

Section: Machine Learning For Building Operation and Maintenancementioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

229

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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Applying IoT and Data Analytics to Thermal Comfort: A Review

Khalil

Esseghir

Merghem-Boulahia

2021

Studies in Computational Intelligence

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Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology

Cited by 149 publications

References 28 publications

A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

State-of-the-art on research and applications of machine learning in the building life cycle

Applying IoT and Data Analytics to Thermal Comfort: A Review

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