Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques

Applied Sciences

Cristina González-Gaya¹  Miguel A. Sebastián¹  Patricia I. Benito¹ 

[1] Department of Construction and Manufacturing Engineering, ETS Ingenieros Industriales, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal 12, 28040 Madrid, Spain;

关键词: thermal comfort;    industrial building;    occupational risk;    Bayesian inference;    sustainability;    energy saving;   

DOI  :  10.3390/app112411979

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown