Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation

Aryal, Ashrant; Becerik-Gerber, Burçin; Lucas, Gale M.; Roll, Shawn C.

doi:10.1109/jiot.2020.3038378

Cited by 31 publications

(14 citation statements)

References 43 publications

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“…Shastri [32] developed a model for Levels of automation for IT services based on Bloom's taxonomy taken as the reference and assessed the automation scope for all the processes of ITIL. Aryal [33] studied heating, ventilation, and air conditioning (HVAC) systems. In this article, he described the development and implementation of an Internet-of-ings (IoT)-based intelligent agent that learns individual occupant comfort requirements and controls the thermal environment using PCS (i.e., a local fan and a heater).…”

Section: Appropriatementioning

confidence: 99%

Selecting the Optimal LoA to Prevent the Expansion of COVID‐19 in the Chemical Industry considering Sustainability Factors: A Fuzzy Mathematical Optimization Approach

Hajghasem

Abtahi

Khalili‐Damghani

et al. 2022

Discrete Dynamics in Nature and Society

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Automation has attracted interest from the industry sector for its potential to improve energy efficiency, cost efficiency, and environmental performance. By elevating the LoA to the highest degree, associated costs will grow accordingly and its implementation will be far more complicated. This will also result in losing workers and decreasing environmental pollutants. On the other hand, increasing power consumption at high levels of automation leads to the production of greenhouse gases. This paper aims to increase the level of automation (LoA) considering the concept of sustainability. This study presents fuzzy multi-objective programming to determine the optimal LoA considering sustainability factors to achieve competitive advantages. To solve the model, the Zimmermann max-min approach was adopted and a cosmetics factory in Iran was chosen to optimize LoA according to this model. The results showed that it is possible to improve the LoA and also consider sustainability factors with the available resources without using the highest LoA. This study can help managers optimize the LoA in their organizations considering the current resources and sustainability issues, and control the company's return on investment and cost of overhead. They can run the model with every definition of LoA proposed till now. This research can benefit the environment and the workers' health in the production line by reducing environmental pollutants and prevent the dismissal of all personnel due to its negative social effects. It also reduces the risk of COVID-19 by minimizing the number of workers. So far, a mathematical model for selecting optimal LoA in the chemical industry considering sustainability has not been presented.

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Section: Appropriatementioning

confidence: 99%

Selecting the Optimal LoA to Prevent the Expansion of COVID‐19 in the Chemical Industry considering Sustainability Factors: A Fuzzy Mathematical Optimization Approach

Hajghasem

Abtahi

Khalili‐Damghani

et al. 2022

Discrete Dynamics in Nature and Society

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“…The auto-regressive with exogenous variables (ARX) model is used to determine the relationships between thermal comfort and collected parameters in order to estimate the PMV index, improving the mean absolute error and the complexity of the prediction when compared to other models of machine learning. 23,24,26,27…”

Section: Introductionmentioning

confidence: 99%

“…Aryal A, Becerik-Gerber B, Lucas M, et al 27 implemented a system with different levels of automation in which the thermal control of the environment is shared between the intelligent system and the user. Using machine learning, the system learns user preferences and controls HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

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

Nascimento

Lopes

2022

Indoor and Built Environment

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The internet of things connects objects to the internet, enabling the dialogue between devices and users, providing new opportunities for applications, such as thermal comfort. In the research, adequate sensors were used to measure the heat index, the thermal discomfort index and the temperature and humidity index based on the temperature and relative humidity of a remote indoor environment. This research evaluated the level of thermal comfort in real-time using tools of storage, processing and analysis of big data information from the collection of IoT devices. With the analysis of the environment, it is possible to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms were also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment. Health researchers, for example, have the necessary knowledge to evaluate clinical data, but they are not used to using data analysis resources and machine learning algorithms. The platform was developed to reduce dependence on data experts and encourage healthcare researchers to develop their own models by automating the steps required for model development, using automated machine learning (AutoML).

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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%

“…, 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.…”

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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Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation

Cited by 31 publications

References 43 publications

Selecting the Optimal LoA to Prevent the Expansion of COVID‐19 in the Chemical Industry considering Sustainability Factors: A Fuzzy Mathematical Optimization Approach

Selecting the Optimal LoA to Prevent the Expansion of COVID‐19 in the Chemical Industry considering Sustainability Factors: A Fuzzy Mathematical Optimization Approach

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

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

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