Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods

Aryal, Ashrant; Becerik-Gerber, Burçin

doi:10.1016/j.buildenv.2020.107316

Cited by 75 publications

(19 citation statements)

References 34 publications

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“…The simulation regarding the impact of the HRV uncertainty of the TSV was made for ± 4 ms. Since HRV measurement through smartwatches is particularly prone to motion artifacts, more simulations were conducted by simulating different HRV uncertainties, which are U = [4,10,20,50, 100] ms. The results of the simulations are shown in Figure 5: the contribution of HRV uncertainty increases after 50 ms, suggesting that up to this value, the uncertainty of the devices that measure HRV is still acceptable for assessing the TSV.…”

Section: Impact Of the Uncertainty On Ai Models For Measuring Human Thermal Comfortmentioning

confidence: 99%

“…In these modern times, it is commonly accepted by the scientific community that the term thermal comfort refers to a very subjective concept, that differs from one individual to another [1]. In fact, thermal comfort is affected by several factors that include personal, psychological, physical and environmental diversities and therefore, the personalization of thermal comfort measurement is increasingly required to provide more tailored and customized indoor environments, that start with the single individual and end with a comfortable and satisfactory environment [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

et al. 2021

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This paper presents an approach to assess the measurement uncertainty of human thermal comfort by using an innovative method that comprises a heterogeneous set of data, made by physiological and environmental quantities, and artificial intelligence algorithms, using Monte Carlo method (MCM). The dataset is made up of heart rate variability (HRV) features, air temperature, air velocity and relative humidity. Firstly, MCM is applied to compute the measurement uncertainty of the HRV features: results have shown that among 13 participants, there are uncertainty values in the measurement of HRV features that ranges from ±0.01% to ±0.7 %, suggesting that the uncertainty can be generalized among different subjects. Secondly, MCM is applied by perturbing the input parameters of random forest (RF) and convolutional neural network (CNN) algorithm, trained to measure human thermal comfort. Results show that environmental quantities produce different uncertainty on the thermal comfort: RF has the highest uncertainty due to the air temperature (14 %), while CNN has the highest uncertainty when relative humidity is perturbed (10.5 %). A sensitivity analysis also shows that air velocity is the parameter that causes a higher deviation of thermal comfort

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Section: Impact Of the Uncertainty On Ai Models For Measuring Human Thermal Comfortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

et al. 2021

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“…The review (Arakawa Martins et al, 2022) pointed to a vast variety of modelling approaches explored in the field, such as Bayesian classification and inference Auffenberg et al, 2018;Lee et al, 2019), Fuzzy Classification using the Wang-Wendel model (Pazhoohesh and Zhang, 2018;Aguilera et al, 2019;Jazizadeh et al, 2014b), and Machine Learning techniques. The latter includes more interpretable approaches such as Classification Trees (Aryal and Becerik-Gerber, 2020), or less transparent but relatively more accurate techniques such as Gaussian Process Classification (Guenther and Sawodny, 2019;Fay et al, 2017), Gradient Boosting Method (Lee and Ham, 2020), Support Vector Machine (Aryal and Becerik-Gerber, 2019;Jiang and Yao, 2016;Lu et al, 2019), Random Forest (Jayathissa et al, 2020;Aryal et al, 2021;Lu et al, 2019), K-Nearest Neighbours (Aryal and Becerik-Gerber, 2019;Aryal et al, 2021) and Artificial Neural Networks (Kim, 2018;Shan et al, 2020). Artificial Neural Networks (ANNs), specifically, have shown promising results.…”

Section: Introductionmentioning

confidence: 99%

“…, 2014b), and Machine Learning techniques. The latter includes more interpretable approaches such as Classification Trees (Aryal and Becerik-Gerber, 2020), or less transparent but relatively more accurate techniques such as Gaussian Process Classification (Guenther and Sawodny, 2019; Fay et al. , 2017), Gradient Boosting Method (Lee and Ham, 2020), Support Vector Machine (Aryal and Becerik-Gerber, 2019; Jung et al.…”

Section: Introductionmentioning

confidence: 99%

Personal thermal comfort models: a deep learning approach for predicting older people’s thermal preference

Martins

Soebarto

Williamson

et al. 2022

SASBE

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PurposeThis paper presents the development of personal thermal comfort models for older adults and assesses the models’ performance compared to aggregate approaches. This is necessary as individual thermal preferences can vary widely between older adults, and the use of aggregate thermal comfort models can result in thermal dissatisfaction for a significant number of older occupants. Personalised thermal comfort models hold the promise of a more targeted and accurate approach.Design/methodology/approachTwenty-eight personal comfort models have been developed, using deep learning and environmental and personal parameters. The data were collected through a nine-month monitoring study of people aged 65 and over in South Australia, who lived independently. Modelling comprised dataset balancing and normalisation, followed by model tuning to test and select the best hyperparameters’ sets. Finally, models were evaluated with an unseen dataset. Accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were used to measure models’ performance.FindingsOn average, the individualised models present an accuracy of 74%, a Cohen’s Kappa Coefficient of 0.61 and an AUC of 0.83, representing a significant improvement in predictive performance when compared to similar studies and the “Converted” Predicted Mean Vote (PMVc) model.Originality/valueWhile current literature on personal comfort models have focussed solely on younger adults and offices, this study explored a methodology for older people and their dwellings. Additionally, it introduced health perception as a predictor of thermal preference – a variable often overseen by architectural sciences and building engineering. The study also provided insights on the use of deep learning for future studies.

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“…Therefore, there have been emerging longitudinally intensive data collection methods in which the goal is to characterize the personal tendencies to feel thermal comfort sensation and preference [8,9]. These individualized models began with work collecting conventional environmental sensor data and longitudinal data from occupants for models to outperform PMV [10,11,12] and grew to include data collection from wearable [13,14] and infrared sensors [15,16].…”

Section: Introductionmentioning

confidence: 99%

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

Abdelrahman,

Miller

2022

Preprint

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Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant's preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with less data. This method uses BIM-extracted spatial data, and Graph Neural Network (GNN) based modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios based on the spatial context of specific spaces and 4 x 4 m grid squares in the building using a theoretical implementation on two field-collected data sets. The results show that the proposed Build2Vec method is 18-23% more in the overall sampling quality than the spaces-based and the square-grid-based sampling methods. The Build2Vec method also has similar performance to the baselines when removing redundant occupant feedback points but with better scalability potential.

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Thermal comfort modeling when personalized comfort systems are in use: Comparison of sensing and learning methods

Cited by 75 publications

References 34 publications

Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

Impact of the measurement uncertainty on the monitoring of thermal comfort through AI predictive algorithms

Personal thermal comfort models: a deep learning approach for predicting older people’s thermal preference

Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models

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