Abstract
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.
Highlights
Industrial Revolution 4.0 is based on Information and Communications Technology (ICT), Internet of Things (IoT), artificial intelligence (AI) linked to Big Data and algorithms used to process them, robotics, and cloud services, among others, to optimize processes and achieve more efficiency and productivity [1,2,3]
The application handbook of Law 13059, Hygrothermal Conditioning of Buildings from the Housing Institute of Buenos Aires, describes that adequate levels of thermal comfort are essential for maintaining health, moderating humidity condensation effects, and saving energy [6]
ICTs, especially those techniques linked to AI use in the Industry 4.0 context, are an opportunity to improve thermal comfort levels—data analysis and calculated probability prediction algorithms from data mining [1,2]
Summary
ISO 7730 Standard defines thermal comfort, known as hygrothermal comfort, as “that mental condition in which thermal environment satisfaction is expressed” [4], which is a subjective concept and very difficult to assess It is even defined by its opposite concept—hygrothermal discomfort, which is climate discomfort due to temperature, humidity, or air velocity. The predictive mean vote is calculated by means of clothing insulation, metabolic rate, and environment characteristics (temperature, radiant temperature, relative humidity, and air velocity) This rate allows the determination of people dissatisfied with the environment, showing major progress in comparison with other thermal comfort rates. Diego-Mas, J.A. [11] considers that the calculation of PMV and PPD allows the identification of thermal discomfort events perceived by the body
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