Predicting personal thermal preferences based on data-driven methods

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.
Original languageEnglish
Publication date2019
Number of pages7
Publication statusPublished - 2019
EventClima 2019: 13th REHVA World Congress - Bucharest, Romania
Duration: 26 May 201929 May 2019
Conference number: 13

Conference

ConferenceClima 2019: 13th REHVA World Congress
Number13
CountryRomania
CityBucharest
Period26/05/201929/05/2019

Cite this

Aguilera Prado, J. J., Toftum, J., & Kazanci, O. B. (2019). Predicting personal thermal preferences based on data-driven methods. Paper presented at Clima 2019: 13th REHVA World Congress, Bucharest, Romania.
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title = "Predicting personal thermal preferences based on data-driven methods",
abstract = "One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70{\%} probability of correct guessing.",
author = "{Aguilera Prado}, {Jos{\'e} Joaqu{\'i}n} and J{\o}rn Toftum and Kazanci, {Ongun Berk}",
year = "2019",
language = "English",
note = "Clima 2019: 13th REHVA World Congress ; Conference date: 26-05-2019 Through 29-05-2019",

}

Aguilera Prado, JJ, Toftum, J & Kazanci, OB 2019, 'Predicting personal thermal preferences based on data-driven methods' Paper presented at Clima 2019: 13th REHVA World Congress, Bucharest, Romania, 26/05/2019 - 29/05/2019, .

Predicting personal thermal preferences based on data-driven methods. / Aguilera Prado, José Joaquín; Toftum, Jørn; Kazanci, Ongun Berk.

2019. Paper presented at Clima 2019: 13th REHVA World Congress, Bucharest, Romania.

Research output: Contribution to conferencePaperResearchpeer-review

TY - CONF

T1 - Predicting personal thermal preferences based on data-driven methods

AU - Aguilera Prado, José Joaquín

AU - Toftum, Jørn

AU - Kazanci, Ongun Berk

PY - 2019

Y1 - 2019

N2 - One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.

AB - One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.

M3 - Paper

ER -

Aguilera Prado JJ, Toftum J, Kazanci OB. Predicting personal thermal preferences based on data-driven methods. 2019. Paper presented at Clima 2019: 13th REHVA World Congress, Bucharest, Romania.