Predictive model of clothing insulation in naturally ventilated educational buildings

Citation: de la Hoz-Torres, M.L.; Aguilar, A.J.; Costa, N.; Arezes, P.; Ruiz, D.P.; Martínez-Aires, M.D. Predictive Model of Clothing Insulation in Naturally Ventilated Educational Buildings. Buildings 2023,13, 1002. https://doi.org/ 10.3390/buildings13041002 Academic Editor: Eusébio Z. E. Conceição Received: 9 March 2023 Revised: 29 March 2023 Accepted: 9 April 2023 Published: 10 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). buildings Article Predictive Model of Clothing Insulation in Naturally Ventilated Educational Buildings María L. de la Hoz-Torres 1, Antonio J. Aguilar 1,* , Nélson Costa 2, Pedro Arezes 2, Diego P. Ruiz 3 and Mª Dolores Martínez-Aires 1 1Department of Building Construction, University of Granada, Av. Severo Ochoa s/n, 18071 Granada, Spain; [email protected] (M.L.d.l.H.-T.); [email protected] (M.D.M.-A.) 2ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; [email protected] (N.C.); [email protected] (P.A.) 3Department of Applied Physics, University of Granada, Av. Severo Ochoa s/n, 18071 Granada, Spain; [email protected] *Correspondence: [email protected] Abstract: Providing suitable indoor thermal conditions in educational buildings is crucial to ensuring the performance and well-being of students. International standards and building codes state that thermal conditions should be considered during the indoor design process and sizing of heating, ventilation and air conditioning systems. Clothing insulation is one of the main factors influencing the occupants’ thermal perception. In this context, a field survey was conducted in higher education buildings to analyse and evaluate the clothing insulation of university students. The results showed that the mean clothing insulation values were 0.60 clo and 0.72 clo for male and female students, respectively. Significant differences were found between seasons. Correlations were found between indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m., and running mean temperature. Based on the collected data, a predictive clothing insulation model, based on an artificial neural network (ANN) algorithm, was developed using indoor and outdoor air temperature, radiant temperature, the temperature measured at 6 a.m. and running mean temperature, gender, and season as input parameters. The ANN model showed a performance of R 2 = 0.60 and r = 0.80. Fifty percent of the predicted values differed by less than 0.1 clo from the actual value, whereas this percentage only amounted to 32% if the model defined in the ASHRAE-55 Standard was applied. Keywords: built environment; educational buildings; thermal environment; clothing insulation; occupant behaviour; natural ventilation 1. Introduction The events of the last few years, including the COVID-19 pandemic and changing climate patterns [ 1 ], have increased concern for ensuring that the indoor environment meets the needs of the building’s occupants. Indoor thermal conditions are considered a critical variable due to their impact on the occupants’ health and work performance [ 2 ]. The importance of thermal conditions in buildings has prompted International Standards to define specific requirements for indoor thermal environmental parameters [ 3 , 4 ]. However, maintaining indoor environmental conditions in accordance with the occupant’s thermal preferences may also lead to a large amount of energy consumption due to the use of heating, ventilation and air conditioning (HVAC) systems [ 5 ]. Indeed, using HVAC systems is the main type of building energy consumption [ 6 , 7 ]. Therefore, the parameters that influence the thermal perception of building users deserve special consideration because they influence indoor environmental conditions and the energy consumption of the building. This situation is particularly relevant in educational buildings, which represent a large part of the building stock and account for a high share of the non-industrial energy con- Buildings 2023,13, 1002. https://doi.org/10.3390/buildings13041002 https://www.mdpi.com/journal/buildings Buildings 2023,13, 1002 2 of 16 sumption of a country [ 8 ]. Thewes et al. [ 9 ] analysed the energy benchmarks of schools in European countries. They concluded that the deviations between countries are mostly not significant and that the electricity consumption values of schools vary from 10 to 30 kWh/(m2a). Part of this energy consumed by educational buildings is used to maintain adequate indoor environmental conditions in classrooms, which is essential due to the significant amount of time that students and teachers spend in these buildings [ 10 , 11 ]. Previous research has shown that poor indoor environmental quality triggers sick buildings syndrome, which leads to poor health conditions, resulting in absenteeism and low productivity in educational institutes [12,13]. Consequently, since the satisfaction and well-being of the occupants of educational buildings may be compromised [ 11 , 14 , 15 ], indoor thermal conditions are critical factors in these buildings’ design and operational process. Building codes have defined different thermal comfort ranges in this context according to the different climatic zones. International Standards [ 3 , 4 ] state that the criteria for the thermal environment should be based on the PMV-PPD thermal comfort indices [ 16 ]. These parameters are calculated based on environmental parameters (relative humidity, air temperature, mean radiant temperature and air speed) and individuals’ characteristics (metabolic rate and clothing insulation). The clothing insulation parameter, which substantially impacts thermal perception, quantifies the amount of thermal insulation a person wears [ 3 , 16 ]. Therefore, it is necessary to know the evaluation of the thermal characteristics of a clothing ensemble (i.e., the thermal insulation) when evaluating the degree of comfort or thermal stress provided by the physical environment according to the standardised method [ 17 ]. According to Standard ISO 7730 [ 16 ], clothing insulation could range between 0 and 2 clo. Nevertheless, if more information is unavailable, clothing insulation should be assumed to be 0.5 clo (e.g., trousers and short sleeves) or 1.0 clo (e.g., trousers, long sleeve shirt and sweater) for the warm and cold seasons, respectively. However, individual occupants’ clothing behaviour influences clothing insulation and, therefore, significantly impacts their thermal comfort. In fact, Newsham [ 18 ] investigated the clothing insulation effect on thermal comfort and energy consumption and concluded that realistic clothing adjustment is an important variable for assessing thermal comfort in non-optimal indoor environments. Gauthier and Shipworth [ 19 ] concluded that clothing insulation, along with the metabolic rate, were the parameters that most influenced PMV-PPD calculations. During typical sedentary activities in educational buildings (e.g., attending a lecture or a class) where 1.2 met can be assumed as a metabolic rate, variations in the level of clothing insulation affects the optimum operative temperature of approximately 6 ◦ C per clo [3,20]. Therefore, the selection of thermal clothing insulation requires special attention because it influences the calculation of the occupants’ thermal sensation and, consequently, the design and sizing of HVAC systems. The two clothing insulation values suggested by the ISO 7730 Standard for warm and cold seasons tend to oversimplify occupant clothing behaviour. The clothing behaviour of building occupants is not constant throughout the seasons but varies according to climatic conditions and is not identical for all individuals [ 21 ]. This underscores the need to investigate the clothing behaviour of building occupants and to develop more accurate models. In this context, previous research has analysed the factors influencing clothing behaviour due to their impact on thermal comfort and proposed models for more accurate clothing insulation estimation. De Carli et al. [ 22 ] analysed people’s clothing behaviour by investigating the external parameters (outside temperature, mean weekly outside temperature and latitude) and indoor parameters (space temperature). They proposed single variable linear regression models to predict clothing insulation based on the outdoor air temperature measured at 6:00 a.m. Schiavon and Lee [ 20 ] developed models to predict clothing insulation based on observations from the RP-921 and ASHRAE RP-884 databases. The first proposed model calculated clothing insulation based on the outdoor air temperature measured at 6:00 a.m., while the second model used the outdoor air temperature measured at 6:00 a.m. and the indoor operating temperature. Zhao et al. [ 23 ] analysed the clothing adaptation of rural Buildings 2023,13, 1002 3 of 16 residents in the cold region of China. They developed predictive clothing insulation models based on the operating temperature and a 7-day running mean temperature as predictors. Rupp et al. [ 24 ] analysed the clothing insulation in ASHRAE Global Thermal Comfort Database I and II. They derived predictive models of ensemble insulation based on indoor air temperature, the season and the building ventilation type. They also concluded that PMV predictions were improved by accounting for chair insulation. Wang et al. [ 25 ] proposed that a neutral clothing insulation model can be used to determine whether clothing adjustment can sufficiently offset indoor temperatures in naturally ventilated building contexts. They found significant differences between the clothing behaviour of male and female occupants, with female occupants more actively adjusting their clothing. Their findings also showed that climate, season, building type and indoor/outdoor temperature variations were the key contextual variables to be considered for understanding occupant clothing behaviour. The influence of this variable was also pointed out by ASHRAE 55, which stated that its adapted predictive clothing insulation model might not be appropriate for all cultures and occupancy types. In this context, this study aimed to develop a predictive clothing insulation model for occupants of educational buildings. For this purpose, an artificial neural network (ANN)- based model was developed based on data collected from field measurement campaigns conducted in higher education buildings in southern Spain. In addition, the analysis developed in this study also aims to understand the environmental and individual factors affecting the clothing behaviour of the occupants of higher educational buildings. 2. Materials and Methods 2.1. Location and Characteristics of the Participants The data used in this study were collected in educational buildings located in the Campus Fuentenueva of the University of Granada, Granada, Spain. There were 6593 students on this campus during the 201/2022 academic year. The climate in Granada is characterised by low rainfall over the year, a very hot and dry summer and the coldest month averaging above 0 ◦ C. Therefore, this climate is classified as Hot-summer Mediterranean (Csa), according to Köppen and Geiger [26]. A questionnaire survey and field measurements were conducted simultaneously in classrooms during university lectures. The buildings on the Campus Fuentenueva are characterised by a concrete frame structure, ceramic façades and aluminium windows. Regarding the finishing material, the floors are finished with terrazzo or natural stone, the ceilings with registrable suspended ceiling systems and the walls with gypsum plaster. The Fuentenueva Campus buildings do not have a mechanical ventilation system, and air circulation relies on windows and doors opening and closing. The field measurements were carried out from September 2021 to July 2022 on different days; each participant only experienced a single condition. The field measurements were carried out in all months between September and July, except the holiday period in December and January when the educational buildings were closed. A total of 2022 university students participated in this study. Table 1shows the participant’s characteristics and their distribution by season. 2.2. Data Collection: Questionnaire Survey and Monitoring Equipment The questionnaire survey and the IEQ monitoring campaign were conducted simultaneously during university lectures (1.5–2 h). The students’ characteristics and state of clothing were collected using paper-based occupant survey questionnaires. The questionnaire was prepared in Spanish and included questions on personal characteristics (age and gender) and clothing insulation state. For this purpose, the students were asked to indicate what clothes they were wearing from a list defined by the ASHRAE [ 3 ]. The questionnaire survey was conducted during the last 15 min of a lecture, and the students remained seated during the class (this protocol ensures that students had been seated for at least 1 h and eliminates the influence of short-term thermal history in transitional Buildings 2023,13, 1002 4 of 16 spaces before attending the lecture class). The questionnaires were filled out during mid-morning or mid-afternoon, not just after arrival or after a lunch break, following the recommendations stated in UNE EN 16798-2:2019 Annex F [27]. Table 1. Participants’ characteristics. Variable Number of Respondents (N, %) Gender Male 1200 (59%) Female 822 (41%) Age 18–24 1741 (86%) 25–30 236 (12%) >30 45 (2%) Season Autumn 707 (35%) Winter 445 (22%) Spring 472 (23%) Summer 398 (20%) Indoor environmental parameters were measured from the beginning to the end of the university lectures, where the questionnaire surveys were carried out. These parameters included the air temperature ( Tair,i ), indoor relative humidity ( RHi ), mean radiant temperature ( Tr,i ) and air velocity ( vi ). The characteristics of the sensors used to collect this data are detailed in Table 2. The sensors were placed at a height of 0.6 m and separated >1 m from the surrounding surfaces in the middle of the classrooms. The sensor location follows the recommendations stated in ASHRAE-55-2020 Standard [ 3 ] and UNE EN-ISO 7726:2002 [28]. The logging interval selected to measure all the parameters was 1 min. Table 2. Sensor characteristics. Parameter Sensor Range Accuracy Air temperature FHAD 46-C41A AHLBORN −20 to +80 ◦C Typical ±0.2 K at 5 to 60 ◦C Maximum ±0.4 K at 5 to 60 ◦C Maximum ±0.7 K at −20 to +80 ◦C Relative humidity FHAD 46-C41A AHLBORN 0 to 98% RH ±2.0% RH in range from 10 to 90% RH ±4.0% RH in range from 5 to 98% RH Mean radiant temperature FPA805GTS AHLBORN –50 to 200 ◦C 0.1 ◦C Air velocity HD403TS2 Delta OHM®0.05 to 5.00 m/s ±(0.03 m/s + 2% f.s.) Outdoor weather parameters (air temperature and relative humidity) were also collected from a meteorological station close to the Fuentenueva Campus, owned by AEMET (the State Meteorological Agency). These outdoor parameters were continuously measured during the study period. The running mean outdoor temperature (Trm) was calculated using (1): Trm =(1−α)nTt−1+αTt−2+α2Tt−3. . . +αn−1Tt−no(1) where Trm is the running mean outdoor temperature at time t , n is the previous time interval, and α is the time constant which reflects the rate at which the effect of any past temperature decays (0 ≤α< 1). In this study, the α value selected was 0.8. A greater effect of outdoor temperatures measured in the previous days is indicated by a higher value of α . 2.3. Data Pre-Processing and ANN-Based Model The recorded indoor/outdoor environmental values, together with the responses collected from the questionnaire, were processed and analysed. The clothing insulation was estimated using the typical clothing insulation values provided by the ASHRAE Buildings 2023,13, 1002 5 of 16 55 Standard [3]. The results were analysed to investigate the relationship between season, gender, and clothing behaviour. Subsequently, artificial neural network (ANN) algorithms were used to develop a model to predict the clothing insulation level. ANN is a machine learning method widely used in many fields, such as science, engineering, education and other industries [ 29 ]. The performance of an ANN model in determining non-standard non-linear relationships between independent and dependent variables is much higher than that of a regression model. For this reason, this mathematical model has been selected. The multilayer perceptron is the structure most used. This ANN structure usually comprises an input, hidden and output layers and many interconnected nodes (or neurons). Previous research has shown that a single hidden layer structure with a sufficient number of neurons can approximate any function with the desired accuracy—including two hidden layers may introduce a higher risk of convergence to a local minimum and rarely improves the model [ 30 , 31 ]. Therefore, the model’s input vector is connected to the input layer. Sigmoid and Levenberg–Marquardt was the selected activation function and back-propagation algorithm, respectively. The dataset was segmented: 60% training, 20% validation and 20% testing. Previous studies have used this procedure to develop ANN-based prediction models [ 32 – 35 ]. The training set serves the purpose of optimising the ANN model. The validation set, on the other hand, is utilised to halt the optimisation process. This is achieved by ceasing the training process when there is no further improvement in the model’s accuracy on the validation set. Lastly, the test set is employed to evaluate the accuracy of the trained model [ 36 ]. The input and output data were normalised to prevent premature saturation and prevent larger numbers from overriding smaller ones. Equation (2) shows the normalised method in this study. xnorm =x−xmin xmax −xmin (2) xnorm is the normalised value, x is the actual value, xmin is the minimum value of the set x and xmax is the minimum value of the set x . The performance of the generated models were evaluated using the mean square error (MSE) and the mean absolute error (MAE) (3) and (4). MSE =1 N n ∑ iyj−zj2(3) MAE =1 N n ∑ i yj−zj (4) Both parameters provide a direct quantification of the predictive error and have been widely used to evaluate the performance of ANN models. Additionally, the coefficient of determination (R2) was also calculated. 2.4. Predictive Clothing Insulation Models The UNE 7730 and ASHRAE 55 standards suggest that clothing insulation can be assumed as 1.0 clo and 0.5 clo for winter and summer conditions, respectively. However, as mentioned in Section 1, this method is very inaccurate for estimating clothing insulation. In addition, another method defined in ASHRAE 55 is the adapted model shown in (5).      Tair(out,6)<−5◦C→IC=1.00 −5◦C≤Tair(out,6)<5◦C→IC=0.818 −0.0364 ·Tair(out,6) 5◦C≤Tair(out,6)<26◦C→IC=10(−0.1635−0.0066 ·Tair(out,6)) 26◦C≤Tair(out,6)→IC=0.46      (5) where Tair(out,6)stands for the outdoor air temperature measured at 6:00 a.m. Buildings 2023,13, 1002 6 of 16 3. Results 3.1. Environmental Conditions As stated in Section 2.1, the climate of Granada is characterised by short, hot and dry summers and long, cold and partly cloudy winters, while the intermediate seasons (i.e., spring and autumn) are periods of more moderate temperatures. Table 3shows the characteristics of the environmental parameters measured during the data collection campaign in each season. During the entire study, the outdoor temperature ranged between 0.00 ◦ C and 36.5 ◦ C, while RH out and vout ranged between 14% and 94% and 0–23 m/s, respectively. Regarding the outdoor temperature measured at 6:00 a.m., the collected minimum and maximum values were 0.0 ◦ C and 25.6 ◦ C, while Trm ranged between 1.8 ◦ C and 14.8 ◦C. Table 3. Outdoor and indoor environmental parameters. Outdoor Parameters Indoor Parameters Tair,out RHout vout Tair (out,6)Trm Tair,in Trad RHin vin Summer Mean 23.4 39.9 6.18 14.6 11.0 25.9 26.4 35.4 0.08 SD 5.1 12.9 5.02 2.2 1.2 2.1 1.9 7.5 0.06 Min 18.3 14.0 1.00 11.6 9.5 22.3 23.6 19.6 0.01 Max 33.7 58.0 23.00 20.0 13.1 29.2 29.2 46.0 0.22 Autumn Mean 11.3 61.1 4.40 6.0 5.3 19.5 19.9 38.6 0.04 SD 6.0 19.1 4.89 4.0 2.4 3.3 3.3 6.7 0.04 Min 2.1 16.0 0.00 0.4 2.6 14.1 14.9 19.6 0.00 Max 30.3 94.0 23.00 14.7 10.4 28.0 28.5 50.1 0.19 Winter Mean 9.8 63.9 2.61 3.2 3.6 17.7 17.9 38.9 0.01 SD 4.5 17.1 4.10 2.3 1.3 2.3 1.9 4.4 0.02 Min 0.0 40.8 0.00 0.0 1.8 14.5 14.7 32.5 0.00 Max 15.0 91.0 20.36 9.5 6.4 22.2 22.0 47.8 0.08 Spring Mean 19.6 52.4 2.56 14.1 9.2 23.1 23.1 41.4 0.04 SD 6.5 19.3 3.06 6.1 3.2 3.5 3.5 8.8 0.03 Min 9.0 14.0 0.00 7.6 5.0 18.1 18.5 21.3 0.01 Max 36.5 93.5 15.10 25.6 14.8 28.9 29.6 54.0 0.11 Regarding the indoor environmental parameters, the mean Tair,in ranged between 14.1–29.2 ◦ C, similar to the minimum and maximum values of Trad (14.8 ◦ C and 29.6 ◦ C, respectively). RHin ranged between 19.6% and 54.0%, and Vi ranged between 0.00 m/s and 0.22 m/s. It should be noted that since the ventilation strategy during this period stated that classrooms had to be naturally ventilated through windows and doors, no window adjustments were observed during the field measurement campaign. Figure 1shows each season’s outdoor and indoor air conditions during the study period. As expected, the seasons with the warmer indoor air temperatures were summer (mean = 25.92 ◦ C, SD = 2.10 ◦ C) and spring (mean = 23.08 ◦ C, SD = 3.57 ◦ C), while the lowest values were recorded during the winter (mean = 17.68 ◦ C, SD = 2.25 ◦ C). The recorded outdoor air temperature values were slightly lower than the indoor air temperature values during the summer. During the other seasons (winter and intermediate spring, and autumn), a larger variation was observed between indoor and outdoor air temperature values. Regarding RH, the outdoor values were higher than those measured indoors, with the lowest values collected during the summer season (mean RHout = 39.94%; SD RHout = 12.85%; mean RHin = 35.43%; SD RHin = 7.48%), and the highest values during the winter season (mean RHout = 63.92%; SD RHout = 17.06%; mean RHin = 38.98%; SD RHin = 4.40%). Similar trends were also observed for air velocity, with higher values measured outdoors during all seasons. It is worth noting that there were large variations between the mean values of Buildings 2023,13, 1002 7 of 16 outdoor air temperature measured at 6:00 a.m. during the different seasons: the mean values of Tair (out,6)were 3.20 ◦C and 14.62 ◦C in the winter and summer seasons, respectively. Figure 1. Variation of the indoor and outdoor environmental parameters by season. 3.2. Clothing Insulation and Gender Figure 2shows the collected values of clothing insulation reported by male and female students. As can be seen, the mean value of clothing insulation reported by male students (mean = 0.60 clo; SD = 0.29 clo) is lower than that reported by female students (mean = 0.72 clo; SD = 0.33 clo). In fact, these values have been analysed, and the results obtained after performing a Kruskal–Wallis test showed that there are statistically significant differences between the two populations (of male and female students) ( χ2 = 60.21; p-value < 0.001). Similar results were already reported by previous studies conducted in educational buildings [21,37–39]. Figure 2. Clothing insulation level by gender. Whiskers stand for minimum and maximum values. 3.3. Clothing Insulation and Seasons The relationship between clothing insulation and the seasons was assessed in this study. Different mean clothing insulation values were obtained for the summer ( mean = 0.35 clo, SD = 0.15 clo), autumn (mean = 0.83 clo; SD = 0.29 clo), winter (mean = 0.73 clo; SD = 0.20 clo), and spring season (mean = 0.46 clo; SD = 0.26 clo). The results revealed statistically significant differences between seasons ( χ2 = 799.76; p-value < 0.001). Figure 3shows the clothing insulation level by season and gender. Buildings 2023,13, 1002 8 of 16 Figure 3. Clothing insulation level by season and gender. M indicates male; F indicates female. Whiskers stand for minimum and maximum values. In addition, this study assessed whether there are differences between the level of clothing insulation of male and female students by season. The obtained values showed evidence of statistically significant differences for all seasons (Table 4). Table 4. Results obtained between the clothing insulation level of male and female students for each season. χ2p-Value Summer 33.554 <0.001 Autumn 27.667 <0.001 Winter 17.646 <0.001 Spring 38.837 <0.001 3.4. Clothing and Environmental Parameters The results in the previous sections revealed significant differences in the levels of students’ clothing insulation in the different seasons and by gender. Subsequently, the relationship between indoor and outdoor environmental parameters and the level of clothing insulation was examined (Table 5). A strong inverse correlation ( ρ < − 0.5) was found between clothing insulation and Tair,out , Tair (out,6) , Trm , Tair,in and Trad . Additionally, the parameters that showed the least correlation were vout ( ρ = − 0.157), RHin ( ρ = − 0.187) and vin ( ρ = − 0.216). Figures 4and 5show the relationship between clothing insulation and the indoor and outdoor environmental parameters, respectively. Table 5. Correlation between clothing insulation and environmental parameters. Outdoor Parameters Indoor Parameters Tair,out RHout Vout Tair (out,6)Trm Tair,in Trad RHin Vin Clothing insulation ρ*−0.625 −0.458 −0.157 −0.634 −0.655 −0.640 −0.640 −0.187 −0.216 p-value ** ** ** ** ** ** ** ** ** *ρindicates Spearman coefficient; ** p< 0.01. Buildings 2023,13, 1002 9 of 16 Figure 4. Relationship between outdoor environmental factors and clothing insulation: ( a ) outdoor air temperature; ( b ) outdoor air RH; ( c ) outdoor air velocity; ( d ) outdoor air temperature measured at 6:00 a.m.; (e) outdoor running mean temperature. The red line indicates the curve fit (95% confidence interval). Figure 5. Relationship between indoor environmental factors and clothing insulation: ( a ) indoor air temperature; ( b ) indoor radiant temperature; ( c ) indoor RH; ( d ) indoor air velocity. The red line indicates the curve fit (95% confidence interval). Buildings 2023,13, 1002 16 of 16 25. Wang, L.; Kim, J.; Xiong, J.; Yin, H. Optimal clothing insulation in naturally ventilated buildings. Build. Environ. 2019 ,154, 200–210. [CrossRef] 26. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 2006,15, 259–263. [CrossRef] 27. UNE-CEN/TR 16798-2:2019; Energy Performance of Buildings—Ventilation for Buildings—Part 2: Interpretation of the Requirements in EN 16798-1—Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics (Module M1-6). UNE: Madrid, Spain, 2019. 28. UNE-EN ISO 7726:2002; Ergonomics of the Thermal Environment—Instruments for Measuring Physical Quantities. (ISO 7726:1998). UNE: Madrid, Spain, 1998. 29. Kumar, A.; Mor, N. An approach-driven: Use of artificial intelligence and its applications in civil engineering. Artif. Intell. IoT Smart Converg. Eco-Friendly Topogr. 2021,85, 201–221. [CrossRef] 30. Tasadduq, I.; Rehman, S.; Bubshait, K. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew. Energy 2002,25, 545–554. [CrossRef] 31. Panchal, G.; Ganatra, A.; Kosta, Y.; Panchal, D. Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layersInternational. J. Comput. Theory Eng. 2011,3, 332–337. [CrossRef] 32. Wei, W.; Ramalho, O.; Malingre, L.; Sivanantham, S.; Little, J.C.; Mandin, C. Machine learning and statistical models for predicting indoor air quality. Indoor Air 2019,29, 704–726. [CrossRef] [PubMed] 33. Das, P.; Shrubsole, C.; Jones, B.; Hamilton, I.; Chalabi, Z.; Davies, M.; Mavrogianni, A.; Taylor, J. Using probabilistic samplingbased sensitivity analyses for indoor air quality modelling. Build. Environ. 2014,78, 171–182. [CrossRef] 34. Elbayoumi, M.; Ramli, N.A.; Yusof, N.F.F.M. Development and comparison of regression models and feedforward backpropagation neural network models to predict seasonal indoor PM2. 5–10 and PM2. 5 concentrations in naturally ventilated schools. Atmos. Pollut. Res. 2015,6, 1013–1023. [CrossRef] 35. Chaudhuri, T.; Soh, Y.C.; Li, H.; Xie, L. A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings. Appl. Energy 2019,248, 44–53. [CrossRef] 36. Gong, P.; Cai, Y.; Zhou, Z.; Zhang, C.; Chen, B.; Sharples, S. Investigating spatial impact on indoor personal thermal comfort. J. Build. Eng. 2022,45, 103536. [CrossRef] 37. De la Hoz-Torres, M.L.; Aguilar, A.J.; Costa, N.; Arezes, P.; Ruiz, D.P.; Martínez-Aires, M.D. Reopening higher education buildings in post-epidemic COVID-19 scenario: Monitoring and assessment of indoor environmental quality after implementing ventilation protocols in Spain and Portugal. Indoor Air 2022,32, e13040. [CrossRef] 38. Aguilar, A.J.; de la Hoz-Torres, M.L.; Martínez-Aires, M.D.; Ruiz, D.P. Thermal perception in naturally ventilated university buildings in Spain during the cold season. Buildings 2022,12, 890. [CrossRef] 39. Wang, Z.; de Dear, R.; Luo, M.; Lin, B.; He, Y.; Ghahramani, A.; Zhu, Y. Individual difference in thermal comfort: A literature review. Build. Environ. 2018,138, 181–193. [CrossRef] 40. Alghamdi, S.; Tang, W.; Kanjanabootra, S.; Alterman, D. Field investigations on thermal comfort in university classrooms in New South Wales, Australia. Energy Rep. 2023,9, 63–71. [CrossRef] 41. Hu, J.; He, Y.; Hao, X.; Li, N.; Su, Y.; Qu, H. Optimal temperature ranges considering gender differences in thermal comfort, work performance, and sick building syndrome: A winter field study in university classrooms. Energy Build. 2022 ,254, 111554. [CrossRef] 42. Talukdar, M.S.J.; Talukdar, T.H.; Singh, M.K.; Baten, M.A.; Hossen, M.S. Status of thermal comfort in naturally ventilated university classrooms of Bangladesh in hot and humid summer season. J. Build. Eng. 2020,32, 101700. [CrossRef] 43. Jowkar, M.; Rijal, H.B.; Montazami, A.; Brusey, J.; Temeljotov-Salaj, A. The influence of acclimatization, age and gender-related differences on thermal perception in university buildings: Case studies in Scotland and England. Build. Environ. 2020 ,179, 106933. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.