Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques
This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for d...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/24/11979 |
| _version_ | 1797506735427026944 |
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| author | Patricia I. Benito Miguel A. Sebastián Cristina González-Gaya |
| author_facet | Patricia I. Benito Miguel A. Sebastián Cristina González-Gaya |
| author_sort | Patricia I. Benito |
| collection | DOAJ |
| description | This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for different pairs of indoor temperature and humidity values according to activity, time, and season, verified in the workplace, were obtained. The results obtained were compared to other statistical models of linear regression used for thermal comfort, thus observing that comfort temperature values are within a same range, yet the network offered more information since a range of options for interior design parameters (temperature/relative humidity) was offered for different work, time, and season conditions. Additionally, if compared with static models of heat exchange, the contribution of Bayesian networks is noted when considering a context of actual operability and adaptability conditions to the environment, which is promising for developing thermal comfort intelligent systems, especially for the development of sustainable settings within the Industry 4.0 paradigm. |
| first_indexed | 2024-03-10T04:36:59Z |
| format | Article |
| id | doaj.art-c2a42dc8fc824dc4b200eaf84c1ab699 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:59Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c2a42dc8fc824dc4b200eaf84c1ab6992023-11-23T03:41:20ZengMDPI AGApplied Sciences2076-34172021-12-0111241197910.3390/app112411979Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference TechniquesPatricia I. Benito0Miguel A. Sebastián1Cristina González-Gaya2Department of Construction and Manufacturing Engineering, ETS Ingenieros Industriales, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal 12, 28040 Madrid, SpainDepartment of Construction and Manufacturing Engineering, ETS Ingenieros Industriales, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal 12, 28040 Madrid, SpainDepartment of Construction and Manufacturing Engineering, ETS Ingenieros Industriales, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal 12, 28040 Madrid, SpainThis paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for different pairs of indoor temperature and humidity values according to activity, time, and season, verified in the workplace, were obtained. The results obtained were compared to other statistical models of linear regression used for thermal comfort, thus observing that comfort temperature values are within a same range, yet the network offered more information since a range of options for interior design parameters (temperature/relative humidity) was offered for different work, time, and season conditions. Additionally, if compared with static models of heat exchange, the contribution of Bayesian networks is noted when considering a context of actual operability and adaptability conditions to the environment, which is promising for developing thermal comfort intelligent systems, especially for the development of sustainable settings within the Industry 4.0 paradigm.https://www.mdpi.com/2076-3417/11/24/11979thermal comfortindustrial buildingoccupational riskBayesian inferencesustainabilityenergy saving |
| spellingShingle | Patricia I. Benito Miguel A. Sebastián Cristina González-Gaya Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques Applied Sciences thermal comfort industrial building occupational risk Bayesian inference sustainability energy saving |
| title | Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques |
| title_full | Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques |
| title_fullStr | Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques |
| title_full_unstemmed | Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques |
| title_short | Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques |
| title_sort | study and application of industrial thermal comfort parameters by using bayesian inference techniques |
| topic | thermal comfort industrial building occupational risk Bayesian inference sustainability energy saving |
| url | https://www.mdpi.com/2076-3417/11/24/11979 |
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