Thermal Comfort for Classrooms by prateek wahi

Thermal Comfort in Classroom Stainslascollege Westplantsoen, Delft

AR0125 Technoledge Climate Design

Thermal Comfort in Classroom Are the classrooms in the Stainslascollege Westplantsoen thermally comfortable ? AR0125 Technoledge Climate Design Hamidreza Shahriari - 4931963 Kiana Mousavi â&#x20AC;&#x201C; 4878736 Prateek Wahi - 4934695

Dr. R.M.J. Bokel April 2019

Content 1. 2. 3. 4. 5. 6. 7.

Introduction Literature Methodology Discussion Improvements Conclusion References Appendix

4 5 6 10 20 28 29 30

Introduction

Thermal comfort in schools is associated with long term effects on comfort, health and learning performance (ter Morsa, Hensen, Loomans, & Boerstra, 2011). It becomes even more complicated since the school typology deals with different age groups with different functionalities. The education system around the globe comprises of teaching methodologies where the student spends the majority of his/her time in the classrooms. Since the classrooms are the quintessential aspects of teaching; it will not be an exaggeration to state that a comfortable classroom does add up to the quality of education provided. Hence, it is imperative to understand the aspect of thermal comfortability into classrooms.

Therefore, to analyse further question is formulated:

following research

“Are the classrooms in the Stanislascollege Westplantsoen thermally comfortable?” To answer the above main research question following sub-questions were defined to proceed with the research. 1. What is the influence of the location of the classroom on the students’ thermal comfort? (whether the classroom is in the old part of the school or the new part). 2. What are the possible causes of discomfort?

As part of the research, an existing school located in Delft is used as a case study to investigate the 3. Which part of the school needs maximum thermal comfort in its classrooms. The studied interventions? classrooms are part of the Stanislascollege Westplantsoen, located in Westplantsoen 71, Delft. The school comprises four levels, and it is built in four parts, the oldest part in 1950, older part in 1970, the newer part in 2000 and the newest block in 2011. The oldest part has natural ventilation, and the temperature can be controlled and adjusted only through the offices; therefore, local control for each class is not possible. The newest part of the building is equipped with a mechanical ventilation system, and the classes have floor heating. Figure 2: Bird’s Eye View. Source: Google Maps

Figure 1: Site plan of the school. Source: Google Maps

Literature Study

2.1 Thermal Comfort According to ASHRAE-55 (2010, p.4), thermal comfort regards to the satisfaction of the state of mind corresponding to the thermal environment of a person who dwells in it. However, this satisfaction differs from one person to another since it is dependent on the physiological and psychological response of the users. To understand the optimal thermal comfort, range it is essential to understand its dependency on the physical factors and the vote of the people dwelling in the space. 2.2 Factors affecting Thermal Comfort According to ASHRAE–55 (2010, p.5), the factors which affect thermal comfort can be listed as: 1. 2. 3. 4. 5. 6.

Metabolic Rate Clothing Insulation Air Temperature Radiant Temperature Air Speed Relative Humidity

These six factors are the fundamental parameters of affecting the thermal comfort of any space where temperature, humidity and speed of the air being the physical factors. Metabolic Rate accounts for the level of activity a person undergoes. According to NEN 7730 Annex B, the classroom activity is considered to have a sedentary activity with 1.2 met or 70 W/m2. Clothing insulations refer to the thermal resistance the clothes provide. These two factors are the personal factors which also accounts for the adaptability of any users in a designated space. 2.3 Guidelines NEN 7730, ISO 74, EN 15251 The NEN 7730 guidelines use empirical method to explain thermal comfort by predicting a mean vote and people dissatisfied in an environment with regards to the physical aspects of a space like air temperature, humidity, air speed and radiant temperature and, activity and clothing of users as mentioned in section 2.3. While EN 15251 is by NEN 7730, it corresponds to the energy consumptions of the indoor thermal environment in non-industrial buildings.

However, the standards mentioned above are empirical; the Dutch standards ISO 74 adopted an adaptive take on achieving thermal comfort. This guideline is based on the Adaptive Temperature Limit Value indicator, ATG. While the PMV method is still practised in cases where the metabolic rate or the clothing insulation is significantly high, the ATG method is proposed to replace the GTO method. This method can be implemented in the buildings regardless of the amount of user influence, whether the windows are openable or even fully-closed facades. The ATG method can be applied either in the design process of a building or when the existing buildings are evaluated. In both cases, the data can be collected from the measurements or simulation calculations. Therefore, by these guidelines, some of the essential factors were understood to investigate the thermal comfort in schools. 2.4 Optimum Temperature Range The EN 15251 suggests the following temperature ranges mentioned in Table 1 for thermal comfort in winter and summer considering the level of activity and clothing insulation. Local discomfort conditions such as vertical air temperature difference, high or low floor temperatures and radiant temperature asymmetry can also affect thermal comfort. Activity Classroom

Operative Temperature (°C)

Maximum Mean Air Velocity (m/s)

W/m2

Summer

Winter

Summer

Winter

24.5 ∓ 1.0

22.0 ∓ 1.0

.12

.10

Table 1: Design criteria for classrooms in summer and winter. Source: EN-15251

2.5 Shortcomings The standards in the current regulations do not seem to be complete. First, while the metabolic rate of the male and female is different, in the existing formulas and calculations, it is the same. Also, only children and old people are considered for the age considerations, while other age ranges are neglected. Another point to consider is the cultural and contextual background of the people. Each person has different preferences, expectations and definitions of their thermal environment. While the temperature of an environment is in the acceptable

03 acceptable range, some people might prefer the temperature to be higher or lower. Also, the cultural and contextual background can affect a personâ&#x20AC;&#x2122;s tolerance towards temperature change. Moreover, the solar radiation and placement of the room can affect the indoor temperature which is not considered in these regulations. Other factors such as air tightness, thermal insulation of the facade and the minimum required amount of ventilation are also important. 2.6 Conclusion In the discussion for thermal comfort standards and conditions in the classrooms, it was understood that the optimal temperature range is needed to analyse the thermal comfortability of the classrooms. For measurements, the physical factors as mentioned in section 2.1 Air temperature, Humidity, AirSpeed will be considered. The Metabolic rate and the temperature range for summers and winters as mentioned in Table 1 will be considered for analysis. Clothing value is depended on personal choice; therefore for the current research analysis, it will be understood via questionnaires about how the users adapt concerning their clothing.

Method

3.1 Research Methodology For the research with the regards to the thermal comfort of the classroom, qualitative and quantitative research methods were used for data collection. 1. Quantitative analysis: For this type, data collection was done by using Hobo Data Loggers and I-button Loggers to record the temperature, relative humidity and the light intensity for two weeks, from March 5th until March 19th. Also, a field study was conducted on March 13th by distributing a questionnaire to the students and the momentary questionnaires to the teachers. Detailed questions were asked in the questionnaire using when, where, how and so forth. 18 classrooms were measured, and 132 students participated in the survey. 2. Qualitative analysis: This method was used through observation and interviews with the teachers and the building manager. After talking to the building manager, we learned that the building has natural ventilation in the oldest part and no local heating for the classrooms in that block as they were controlled through the offices. In the newest part, the classes had floor heating and mechanical ventilation system. By talking to the teachers, we understood that they were not satisfied with the conditions of the classes on the third floor in the newest part. They mentioned that the architecture of the building is excellent but it does not fulfil their needs. After the data was collected from the classrooms, surveys were then divided according to the clusters of classes. These clusters were based upon when they were constructed namely oldest, older, newer and newest. The analysis of data over these clusters was then divided into three main stages : 1. Identification of the classrooms exhibiting discomfort. 2. Distinguishing the cause of the discomfort. 3. Finding solutions using simulations.

Figure 3: Methodology chart. Source: Authors.

Figure 3 explains the paradigm followed to answer

the questions stated in section 1. Under the • Students were asked to fill in the survey five identification phase, the data collected was minutes before the break so that they spend compared and analysed to discover problems at least 30 minutes in the class. In this way, phased in relation with discomfort in classrooms. In their responses were not influenced by other the second phase, these problems were discussed environments. further to find the possible causes, which were then again validated with the data collected. The • A time plan was set so that each class fills in the solutions were then based upon these causes which survey at a specific hour. By dividing the school need further validation through software simulations. hours to the classrooms, the whole hours were These simulations were also correlated with the covered. temperature graphs to enable scientific validity. Finally, conclusion and recommendation were made • Some of the questions in the questionnaire were for the overall research project. related to each other. For example, it was asked if they felt hot or cold and the next question was if 3.2 User Survey they wanted any change of temperature. In some classes, although most of the students were The surveys were designed to understand and feeling cold, they wanted the temperature to be quantify the user’s perception of space and whether lowered. As a result, this data was not reliable. they feel thermal comfortable in the classrooms. The chosen classrooms are mentioned in fig 7. For the • Taking eight classes for the user surveys to have same, the questionnaires were divided into three a large sample size. aspects namely: • Questions with multiple answers were not 1. Momentary Questions: These questions were considered. designed to understand the user’s experience of comfort during the time of the survey. • Measurements and user perception survey were compared to identify the cause and propose 2. Temperature Need: These type of questions solutions. were designed to understand users need for a comfortable temperature concerning their 3.3 Measurements experience. The subjective variables tested were Thermal comfort, Thermal Preference and As mentioned in section 2.2 the measurements Draught experience. were done to assess the physical aspects of thermal comfort namely Air Temperature, Relative Humidity, 3. Adaptability: These questions were asked Light Intensity and Dew Point. to understand whether the occupants adapt according to the changing conditions to achieve 3.3.1 Instruments and Data Collection thermal comfort. Data Loggers were synced to start logging at the These surveys were done online for efficient same minute and were programmed to measure the distribution among the students at a specific time. temperature every ten minutes. Before the placement The data was then collected and analysed. A sample of the Data Loggers in the classrooms, they were survey is attached in appendix (A). all put in the same place to measure the difference in temperature between each Data Logger. The 3.2.1 Measures taken for Scientific Rigour accuracy and resolution of the instruments are mentioned in fig 4-6. In order to increase the validity of the data and its The accuracy of the I-Button temperature sensors reliability, the following steps were taken for the is ±1°C and the temperature resolution is 0.125°C questionnaire: (source: https://www.maximintegrated.com).

Hobo Data Logger Accuracy

0.35 °C (0 °C < T< 50 °C )

Figure 4: Hobo Data Logger. Source: Onsetcomp.com

Figure 5: I-Button Reader. Source: Maximintegrated.com

I-Button Accuracy

∓ 1 °C

Resolution

.125 °C

Figure 6: I-Button. Source: Thermochorn.com

The other used devices were Hobo Temperature/ Relative Humidity/Light/External Data Logger. The accuracy of this logger is ± 0.35°C from 0° to 50°C for temperature. For humidity, the accuracy of the device is ± 2.5% from 10% to 90% RH typical, to a maximum of ±3.5% including hysteresis at 25°C (77°F); below 10% and above 90% ±5% typical (source: https://www.onsetcomp.com). 3.3.2 Execution Plan

For the study, 18 classrooms were chosen according to EN 15251 in different orientations and in areas which were expected to be thermally problematic (fig 7). To have the possibility of comparing, at least two classrooms were chosen in each cluster. Data Loggers were installed in the classrooms to measure the temperature for 14 days. For the measurement, 10 Hobo Data Loggers and 8 I-Button loggers were used. According to Adaptive thermal comfort: principles and practice by Nicol, Humphreys and Roaf (2012), the measurements should take place with a distance of 50 cm from the wall and at the height of 60 cm.

However, because of the practical issues and the possibility of the devices being removed or damaged by the students, the places that were considered safe were chosen, mostly near the teacher’s desk or on top of a drawer. Also, it was noted that the devices would not be affected by the sun; therefore, they were placed in the shadow. 3.3.3 Measures taken for Scientific Rigour In order to increase the validity of the data and its reliability, the following steps were taken for the questionnaire and the measurements: • Eighteen classrooms were measured for the sample. • In order to prevent the devices being affected by the sunlight, the devices were put in the shadow. • The devices were hidden from the students, to prevent possible manipulations or the risk of damaging or removing the devices. 3.4 Limitations For students’ questionnaires (see in Appendix A), we decided to use the Collector website to compile the data. While distributing the teachers’ momentary questionnaires, we stumbled upon a few issues. Since the link was very long, many students had problem accessing the web page and they gave up trying after a few times. At the end, we had to shorten the length of the link of the survey using an online website so that the students could access it. Another problem was that because we couldn’t take the time of the class, we could only give the shortened link to the teachers so that they pass it on to the students. Therefore, we could not speculate how many students were going to fill in the questionnaire and we realized that having a printed version would have been more practical since we could collect them ourselves after a while. While distributing the teachers’ momentary questionnaires, some classrooms were found empty and no classes were held. As a result, we had to eliminate those classes from the samples.

Oldest (1950â&#x20AC;&#x2122;s) Older (1970â&#x20AC;&#x2122;s) Newer (2000) Newest (2010) Measurement Survey

Figure 7: Placement of the simple classrooms.

04 Another issue was that a time plan was prepared for each class so that the questionnaire gets filled in at that exact time frame. For example, the third hour was allocated to a specific class and the derived data was supposed to show the results at that time. However, some teachers forgot to ask students to answer the questions at the expected time frame. Also, an important factor was that the students and the teacher spend at least half an hour in the classroom so that results be reliable. For this reason, we asked them to answer the questions five minutes before the break. However, we found out that some classes filled in the questionnaire right after arriving to the class.

Results and Discussion

The analysis was carried out by first clustering the building into groups. As shown in Figure 7, the school is divided into four clusters of the oldest part, the older, the newer and the newest part. These clusters were then modeled into Design Builder and were simulated till it forms a correlation with the temperatures recorded through the devices mentioned in section 3.3.1. The user survey made for the selected classrooms in each cluster were then compared with temperature recorded. The comparative analysis made between surveys and temperature were used to understand the underlying problems. In the following parts, each cluster will be discussed. 4.1 Measurement results Cluster: Oldest Part Orientation: North East Class Surveyed: 213, 219 Class Measured: 113, 119, 211, 213, 219

Figure 8. Source: Authors.

Figure 10. Source: Authors.

Figure 9. Source: Authors.

05-03-19 12:00 05-03-19 21:00 06-03-19 2:10 06-03-19 7:20 06-03-19 12:30 06-03-19 17:40 06-03-19 22:50 07-03-19 4:00 07-03-19 9:10 07-03-19 14:20 07-03-19 19:30 08-03-19 0:40 08-03-19 5:50 08-03-19 11:00 08-03-19 16:10 08-03-19 21:20 09-03-19 2:30 09-03-19 7:40 09-03-19 12:50 09-03-19 18:00 09-03-19 23:10 10-03-19 4:20 10-03-19 9:30 10-03-19 14:40 10-03-19 19:50 11-03-19 1:00 11-03-19 6:10 11-03-19 11:20 11-03-19 16:30 11-03-19 21:40 12-03-19 2:50 12-03-19 8:00 12-03-19 13:10 12-03-19 18:20 12-03-19 23:30 13-03-19 4:40 13-03-19 9:50 13-03-19 15:00 13-03-19 20:10 14-03-19 1:20 14-03-19 6:30 14-03-19 11:40 14-03-19 16:50 14-03-19 22:00 15-03-19 3:10 15-03-19 8:20 15-03-19 13:30 15-03-19 18:40 15-03-19 23:50 16-03-19 5:00 16-03-19 10:10 16-03-19 15:20 16-03-19 20:30 17-03-19 1:40 17-03-19 6:50 17-03-19 12:00 17-03-19 17:10 17-03-19 22:20 18-03-19 3:30 18-03-19 8:40 18-03-19 13:50

05-03-19 12:00 05-03-19 21:20 06-03-19 2:50 06-03-19 8:20 06-03-19 13:50 06-03-19 19:20 07-03-19 0:50 07-03-19 6:20 07-03-19 11:50 07-03-19 17:20 07-03-19 22:50 08-03-19 4:20 08-03-19 9:50 08-03-19 15:20 08-03-19 20:50 09-03-19 2:20 09-03-19 7:50 09-03-19 13:20 09-03-19 18:50 10-03-19 0:20 10-03-19 5:50 10-03-19 11:20 10-03-19 16:50 10-03-19 22:20 11-03-19 3:50 11-03-19 9:20 11-03-19 14:50 11-03-19 20:20 12-03-19 1:50 12-03-19 7:20 12-03-19 12:50 12-03-19 18:20 12-03-19 23:50 13-03-19 5:20 13-03-19 10:50 13-03-19 16:20 13-03-19 21:50 14-03-19 3:20 14-03-19 8:50 14-03-19 14:20 14-03-19 19:50 15-03-19 1:20 15-03-19 6:50 15-03-19 12:20 15-03-19 17:50 15-03-19 23:20 16-03-19 4:50 16-03-19 10:20 16-03-19 15:50 16-03-19 21:20 17-03-19 2:50 17-03-19 8:20 17-03-19 13:50 17-03-19 19:20 18-03-19 0:50 18-03-19 6:20 18-03-19 11:50 18-03-19 23:10

30.0

25.0

20.0

15.0

10.0

5.0

0.0

Class 113 class 213

Class 113

class 211 class 219

Room 213 becomes cold during winters. Graph 1 indicates that room 213 had higher temperatures during the school hours. Since during the field research of the school it was observed that the

Outdoor temperature

Class 119

comfort

Outdoor temperature comfort

Graph 1. Quantitative analysis for North-East cluster. Source: Authors.

heating system of the North East faรงade was centrally controlled by the offices, a simulation was performed to understand the behavior of the classroom without its occupants.

30.0

North East

25.0

20.0

15.0

10.0

5.0

0.0

Class 113-no occiupants

Graph 2. Class 113 without occupants. Source: Authors.

05-03-19 12:00 05-03-19 20:50 06-03-19 1:50 06-03-19 6:50 06-03-19 11:50 06-03-19 16:50 06-03-19 21:50 07-03-19 2:50 07-03-19 7:50 07-03-19 12:50 07-03-19 17:50 07-03-19 22:50 08-03-19 3:50 08-03-19 8:50 08-03-19 13:50 08-03-19 18:50 08-03-19 23:50 09-03-19 4:50 09-03-19 9:50 09-03-19 14:50 09-03-19 19:50 10-03-19 0:50 10-03-19 5:50 10-03-19 10:50 10-03-19 15:50 10-03-19 20:50 11-03-19 1:50 11-03-19 6:50 11-03-19 11:50 11-03-19 16:50 11-03-19 21:50 12-03-19 2:50 12-03-19 7:50 12-03-19 12:50 12-03-19 17:50 12-03-19 22:50 13-03-19 3:50 13-03-19 8:50 13-03-19 13:50 13-03-19 18:50 13-03-19 23:50 14-03-19 4:50 14-03-19 9:50 14-03-19 14:50 14-03-19 19:50 15-03-19 0:50 15-03-19 5:50 15-03-19 10:50 15-03-19 15:50 15-03-19 20:50 16-03-19 1:50 16-03-19 6:50 16-03-19 11:50 16-03-19 16:50 16-03-19 21:50 17-03-19 2:50 17-03-19 7:50 17-03-19 12:50 17-03-19 17:50 17-03-19 22:50 18-03-19 3:50 18-03-19 8:50 18-03-19 13:50

North East

class 211

class 213

12 Outdoor temperature

Outdoor temperature

From the graphs 3-4 it was noted that the rooms without their occupants, in this cluster, were colder. We can conclude that firstly, occupants do add some heat to the overall environment which leads to the increase of the temperature. Secondly, since the heating system is centrally controlled by the offices, it can be said that the offices are colder which causes them to increase the temperature to achieve comfort. This results in the increase of the temperature in the classrooms. comfort

comfort

class 211-no occiupants

Graph 3. Class 211 without occupants. Source: Authors.

25.0

North East

20.0

15.0

10.0

5.0

0.0

cass 213-no occiupants

Graph 4. Class 213 without occupants. Source: Authors.

To discuss about the rooms being colder without their occupants, a comparison was made between room 113 and 213. The results of the measurement devices and simulations were compared, and it was noticed that the heat loss through the walls remains the same but the heat loss due to infiltration was higher in room 213 as compared to 113. The simulation suggested that this could be possible due to one of the ventilations grill of room 113 being closed (This will be further discussed in ***********)

Cluster: Older Part Orientation: South East , North Class Surveyed: 177, 277, 257 Class Measured: 177, 277, 254, 257

Figure 11: Classrooms 177. Source: Authors.

30.0

South East Figure 13: Plan arrangement of classes 177 and 277. Figure 12: Classrooms 277. Source: Authors. Source: Authors.

25.0

20.0

15.0

10.0

5.0

0.0

Class 177

Class 277

Outdoor temperature

comfort

Graph 5. Quantitative analysis for the South East cluster. Source: Authors.

According to the measurements in these classes, the problem is that they become too cold during winters. Also, the classes are colder when there is no sun. The possible reasons could be that in summer the south east orientation may cause higher solar gain causing to rise the temperatures. In winters the facade could have low insulations. Room 277 is at the top floor which could result in heat loss through the roof as well. Another reason could be insufficient heating capacity of heaters.

Cluster: Newer Part Orientation: South East Class Surveyed: 033 Class Measured: 033 From the temperature measurements, it can be concluded that room 033 becomes warm in general (see Graph 5). It was also observed that the classrooms were colder without the students. Therefore, it was concluded that the occupants also add to the temperature rise for the classrooms making it warmer than required. As compared to the oldest block, this classroom was also controlled centrally for the heating system.

25.0

20.0

15.0

10.0

5.0

0.0

Class 33 Tempt

Outdoor Tempt

comfort

Graph 6. Quantitative Analysis for class 33. Source: Authors.

Cluster: Newest Part Orientation: South Class Surveyed: 171,172 Class Measured: 171,172,272,371,372,373,374

Figure 14. Source: Authors.

Figure 15. Source: Authors.

Class 172

Class 371

Class 372

South Oriented classes

class 373

Class 272

Figure 16. Source: Authors.

22.0

17.0

12.0

7.0

2.0

class 171

Outdoor temperature

comfort

Graph 7. Quantitative Analysis for South cluster. Source: Authors.

There were some limitations during the studying of this cluster. The data on the second week was not usable since the temperature shows that the heating is on continually till 1 AM and we did not have any reason about why that is the case. Although the third floor classes were perceived as the most problematic ones, we did not have any questionnaire data from these classes. In winter, there can be too much heat loss through the ceiling of the last floor. Also, the heating capacity in winter is not sufficient and is not calculated according to the class conditions. In summer, the South orientation causes high solar gain which leads to a rise in the temperature.

Cluster: Older Part Orientation: North West Class Surveyed: Class Measured: 034 No survey was collected from this room. However, it was the best class regarding the thermal comfort of the occupants according to the measurements (see Graph 8). This can be because of the sufficient amount of heating and having an on-time set point. Also, this class does not have a significant rise in the temperature due to low solar gain and the heat loss due to receiving the prevailing wind is low.

Figure 17. Source: Authors. 25.0

20.0

15.0

10.0

5.0

0.0

Class 34

Outdoor temperature

comfort

Graph 8. Quantitative Analysis Room 034. Source: Authors.

The classrooms are also centrally controlled by the offices. Therefore if the offices are colder they would increase their heating, which will result in heating of the classrooms making it uncomfortable. Through analysis it was also noticed that there is significant amount of heat loss through ventilation grills which would make the room colder, thus resulting in need for extra heating requirements.

4.2 Questionnaire results Cluster: Oldest Part Orientation: North East Class Surveyed: 213, 219 Class Measured: 113, 119, 211, 213, 219 Slightly warm 70%

Neutral

Slightly Cold

64%

60% 50% 39%

38%

40% 30% 18%

20% 10%

15%

Cluster: Older Part Orientation: South East Class Surveyed: 177, 277 Class Measured: 177, 277 Slightly warm

Class 213

Neutral

Slightly Cold

70%

Class 219

Graph 9. User’s Feeling during survey. Source: Author

59%

60%

52%

50% Warmer

Neutral

Cooler

40%

70%

30%

58%

60%

54%

20%

50%

23%

18%

14%

10%

40% 30%

34%

31%

Class 177

25% 17%

20%

Graph 12. User’s Feeling during survey. Source: Author

15%

10%

Warmer

Class 213

Too Warm/Winters

Class 219

80%

Too Cold/Winters

Too Warm/Summer

Too Cold/Summers 76%

72%

50%

38%

40% 23%

18%

10%

10% 0%

40%

Class 177

30%

27% 20%

20%

23%

1% Class 213

Class 277

Graph 13. User’s desire for temperature. Source: Author

10% 0%

52%

50%

20%

50%

Cooler

59%

60%

30%

70% 60%

Neutral

70%

Graph 10. User’s desire for temperature. Source: Author

30%

Class 277

Class 219

Graph 11. Issue of discomfort in classrooms

From the graphs and the temperature reading it can concluded that room 213 becomes cold during winters while slightly warm during summers. Room 219 becomes during summers. It was also observed that the classrooms were colder without the students. Therefore, it was concluded that the occupants also add to the temperature rise for the classrooms making it warmer then required.

Too Warm/Winters

Too Cold/Winters

Too Warm/Summer

Too Cold/Summers

100%

90%

90% 80%

70%

65% 57%

60% 50% 40% 30%

29%

20%

10%

10% 0%

1% Class 177

1% Class 277

Graph 14. Issue of discomfort in classrooms

For these classes, the problem is that they become too cold during winters and too warm during summers. Also, the classes are colder when there is no sun. The possible reasons could be that in summer the south east orientation may cause higher solar gain causing to rise the temperatures. In winters the facade could have low insulations. Room 277 is at the top floor which could result in heat loss through the roof as well. Another reason could be insufficient heating capacity of heaters.

From the graphs it can be concluded that room 033 becomes warm in general. It was also observed that the classrooms were colder without the students. Therefore, it was concluded that the occupants also add to the temperature rise for the classrooms making it warmer then required. As compared with the oldest block this classroom also controlled centrally for heating systems.

Cluster: Newer Part Orientation: South East Class Surveyed: 033 Class Measured: 033

Cluster: Newest Part Orientation: South Class Surveyed: 171,172 Class Measured: 171,172,272,371,372,373,374

Slightly warm 50%

Neutral

Slightly warm

Slightly Cold

44%

45% 35%

31%

25%

30%

19%

20% 10%

29%

28%

18%

10%

5% 0%

Class 033

Graph 15. User’s Feeling during survey. Source: Author Warmer

Neutral

Class 171

Class 172

Graph18. User’s Feeling during survey. Source: Author Warmer

Cooler

38%

40%

70%

37%

35%

Neutral

Cooler

64%

60%

30%

53%

50%

25%

41%

40%

20%

30%

15% 10%

20%

10%

Too Warm/Winters

Too Cold/Winters

Too Warm/Summer

100%

21% 14% 6%

Class 033

Graph 16. User’s desire for temperature. Source: Author Too Cold/Summers

Class 171

Class 172

Graph 19. User’s desire for temperature. Source: Author Too Warm/Winters

Too Cold/Winters

Too Warm/Summer

93%

59%

60%

80% 70%

50%

60%

40%

50% 31%

50%

21%

20%

20% 1% Class 033

Graph 17. Issue of discomfort in classrooms

10% 0%

41%

36%

30%

31%

10%

Too Cold/Summers

70%

90%

29%

20%

15%

43%

40%

30%

Slightly Cold 53%

50%

40%

Neutral

60%

18%

Class 171

Class 172

Graph 20. Issue of discomfort in classrooms

The user surveys of room 171, located at the newest part of the building at South, showed that the class was cold in winters and warm in summers. The number of people feeling slightly cold might be due to the draught that occurs. The class gets warm in summer due to the South orientation. Although 43% of the students felt slightly cold, 64% of them didnâ&#x20AC;&#x2122;t want any change in the temperature. This can be because the temperature of the class is in the acceptable range. There are some points around 3-4 P.M. when the temperature goes higher than 22 Ë&#x161;C. This can be due to the reason that during winter, the location of the sun at that time reaches the orientation of the windows in the class The temperature data also suggested that the classes on the third floor were the worst in terms of thermal comfortability. As these classes were too cold. The possible reasons could be because of too much heat loss from the ceilings. 4.3 Measurement & questionnaire comparison By comparing the results of the measurements and questionnaires, it can be seen that for almost all the classrooms, these data support each other and it is clear that these classes are not thermally comfortable, due to various possible causes mentioned in the previous sections. According to the measurements, the only classroom which complies with the acceptable temperature range is the class 034.

Improvements

The results of the user survey and temperature measurements identify the problems associated with the thermal comfort of the classrooms. To suggest and validate improvements the four clusters were modelled into design-builder to simulate an existing condition first. 5.1 Cluster : Oldest Part (1950) • Orientation : North East • Class Surveyed : 213,219 • Class Measured : 113,119,211,213,219

Construction • External Walls: Brickwork single leaf construction with insulation with R-value of 1.1 • Internal Partitions: Internal wall sub-surface construction • Internal Thermal Mass: Project thermal mass with zone capacitance of 3 • To simulate that natural ventilation grills are always open we used Airtightness component. • We defined a Model infiltration with constant rate of 3 ac/h with 24/7 schedule. Openings • Glazing Type – Dbl LoE (e2=1) Clr 6mm/13 mm Arg • Type: Fixed with and height • Window with (m) : 0.75 m • Window height: 1.5 • Window spacing: 1.25 • Shading: NO Lighting

Figure 18: North-east Cluster. Source: Design builder simulations

Design Builder Parameters General • • • • • •

Activity • • • • •

Occupancy Density : 0.55 people/m Metabolic Activity ; Reading Seated Office Equipment gain : 1.77 W/m2 Lighting Target Luminance : 280 Lux Since we wanted to simulate a real situation were radiator working continuously we had to define a heat gain source with power density of 75 w/m2 that works with the class heating schedule. 2

• • • •

General Lighting: ON Normalized Power Density: 5 W/m2 – 100lux Luminaire Type: Suspended Working place height: 0.80m

HVAC • All is off The simulated temperature values from the cluster model in design builder was compared with the temperature readings of Hobo from 5th March till 18th March. The above mentioned settings and schedules were used to achieve a correlation for room 113 and 213 more then .75 as shown in Appendix B. 5.1.1 Improvement 1 As first improvement the heating point could be set from 4:00 AM to 6:00 AM. Also, the heating power can be controlled according to the schedule of occupancy of the students in the classroom. It was observed that the occupants also contribute towards heat gain for the classrooms. Therefore, less heating during class hours (graph 21)..

Graph 21: Scheduled heating according to occupancy. Source: Authors.

Fig 19: Arduino Setup for real time control. Source: Authors.

Fig 20: Smart system for ventilation grill control. Source: Authors.

5.1.2 Improvement 2

Design Builder Parameters

For the heat loss through the ventilation grills, a smart system could be used which is controlled by the occupancy and temperature. A concept of the ventilation grill can be seen in Figure 19-20, where the Arduino setup could be used to check the occupancy and temperature using sensors and control the grills. The Arduino could also be used to find the real time schedule; therefore, the building manager could use it for better control of the system.

General

Upon applying this, a simulation was run to check its effectiveness. The comparison between Figure 22 (before using the Arduino system) and Figure 23 (after using the Arduino system) shows that the heat loss through ventilation is reduced which could make the system energy efficient as well. 5.1.3 Improvement 3 Finally, a heating setting according to the new results were adopted from last improvements. The heating power was changed to 52 KW/m2 and along with the heating schedule to start from 7 AM (graph 22). 5.2 Cluster : Older Part (1970) • Orientation : North • Class Surveyed : 257 • Class Measured : 254,257

• • • • • •

Location : Amsterdam, Netherlands Latitude : 52.00 ° Longitude : 5.50 ° Location Template: Rotterdam the Hague ASHRAE Climate zone : Unknown. A new weather file was used using the weather data obtained from KNMI.nl for 5th of March to 18th of March. Because of difference in heating schedule seen in the graph in the middle of the second week we only used this data to simulate the classes for the first week from 5th of March to 14th of March.

Activity • • • • •

Occupancy Density : 0.55 people/m2 Metabolic Activity ; Reading Seated Office Equipment gain : 1.77 W/m2 Lighting Target Luminance : 280 Lux Since we wanted to simulate a real situation were radiator working continuously we had to define a heat gain source with power density of 40 w/m2 that works with the class heating schedule.

Construction • External Walls: Fully filled 50mm-min .wool u=0.53 • Internal Partitions: Internal wall sub-surface construction • Internal Thermal Mass: Project thermal mass with zone capacitance of 3 • To simulate natural ventilation We defined a Model infiltration with constant rate of 3 ac/h. Openings

Figure 21: North Cluster. Source: Design builder simulations

• Glazing Type – Dbl LoE (e2=1) Clr 6mm/13 mm Arg • Type: Fixed with and height • Window with (m) : 1 m • Window height: 1.5 • Window spacing: 1.15 • Shading: NO

Fig 22: External infiltration as a source of heat loss in class 113. Source: Authors.

Fig 23: Reduced heat loss due to infiltration in class 113. Source: Authors.

Graph 22: Optimisation of class 113 after using the smart ventilation control system. Source: Authors.

Fig 24: External infiltration as a source of heat loss in class 254. Source: Authors.

Lighting

Activity

• • • •

• • • • •

General Lighting: ON Normalized Power Density: 5 W/m2 – 100lux Luminaire Type: Suspended Working place height: 0.80m

HVAC • All is off The simulated temperature values from the cluster model in design builder was compared with the temperature readings of Hobo from 5th March till 18th March. The above mentioned settings and schedules were used to achieve a correlation for room 254 more then .75 as shown in Appendix B 5.2.1 Improvement 1 This classroom has heat loss because of ventilation grills and heating power is not enough. To improve the situation Arduino solution was tried by changing the natural ventilation schedule (fig 24-25). After that heating power was changed and reduced to 20 W/ m2 (graph 23). 5.3 Cluster : Older (1970) • Orientation : South - East • Class Surveyed : 177 , 277 • Class Measured : 177 , 277 Design Builder Parameters General • • • • • •

Occupancy Density : 0.55 people/m2 Metabolic Activity ; Reading Seated Office Equipment gain : 1.77 W/m2 Lighting Target Luminance : 280 Lux Since we wanted to simulate a real situation were radiator working continuously we had to define a heat gain source with power density of 25 w/m2 that works with the class heating schedule.

Construction • External Walls: Cavity wall u=0.43 • Internal Partitions: Internal wall sub-surface construction • Internal Thermal Mass: Project thermal mass with zone capacitance of 3 • To simulate natural ventilation We defined a Model infiltration with constant rate of 3 ac/h. Openings • • • • • •

Glazing Type – Dbl LoE (e2=1) Clr 6mm/13 mm Arg Type: Fixed with and height Window with (m) : 1 m Window height: 1.5 Window spacing: 1.15 Shading: NO

Lighting • • • •

General Lighting: ON Normalized Power Density: 5 W/m2 – 100lux Luminaire Type: Suspended Working place height: 0.80m

HVAC • All is off 5.3.1 Improvement 1 To improve thermal conditions of the class 277, we added more insulation to the walls and reduced the U-value to 0.2 w/km2 and we changed the heating schedule in the beginning of he week to start heating sooner, the results are as shown in the below(graph 24).

03/06/19 04:20:00 AM 03/06/19 07:40:00 AM 03/06/19 11:00:00 AM 03/06/19 02:20:00 PM 03/06/19 05:40:00 PM 03/06/19 09:00:00 PM 03/07/19 12:20:00 AM 03/07/19 03:40:00 AM 03/07/19 07:00:00 AM 03/07/19 10:20:00 AM 03/07/19 01:40:00 PM 03/07/19 05:00:00 PM 03/07/19 08:20:00 PM 03/07/19 11:40:00 PM 03/08/19 03:00:00 AM 03/08/19 06:20:00 AM 03/08/19 09:40:00 AM 03/08/19 01:00:00 PM 03/08/19 04:20:00 PM 03/08/19 07:40:00 PM 03/08/19 11:00:00 PM 03/09/19 02:20:00 AM 03/09/19 05:40:00 AM 03/09/19 09:00:00 AM 03/09/19 12:20:00 PM 03/09/19 03:40:00 PM 03/09/19 07:00:00 PM 03/09/19 10:20:00 PM 03/10/19 01:40:00 AM 03/10/19 05:00:00 AM 03/10/19 08:20:00 AM 03/10/19 11:40:00 AM 03/10/19 03:00:00 PM 03/10/19 06:20:00 PM 03/10/19 09:40:00 PM 03/11/19 01:00:00 AM 03/11/19 04:20:00 AM 03/11/19 07:40:00 AM 03/11/19 11:00:00 AM 03/11/19 02:20:00 PM 03/11/19 05:40:00 PM 03/11/19 09:00:00 PM 03/12/19 12:20:00 AM 03/12/19 03:40:00 AM 03/12/19 07:00:00 AM 03/12/19 10:20:00 AM 03/12/19 01:40:00 PM 03/12/19 05:00:00 PM 03/12/19 08:20:00 PM 03/12/19 11:40:00 PM 03/13/19 03:00:00 AM 03/13/19 06:20:00 AM 03/13/19 09:40:00 AM 03/13/19 01:00:00 PM 03/13/19 04:20:00 PM 03/13/19 07:40:00 PM 03/13/19 11:00:00 PM 03/14/19 02:20:00 AM 03/14/19 05:40:00 AM 03/14/19 09:00:00 AM 03/14/19 12:20:00 PM 03/14/19 03:40:00 PM 03/14/19 07:00:00 PM 03/14/19 10:20:00 PM 03/15/19 01:40:00 AM 03/15/19 05:00:00 AM 03/15/19 08:20:00 AM 03/15/19 11:40:00 AM 03/15/19 03:00:00 PM 03/15/19 06:20:00 PM 03/15/19 09:40:00 PM 03/16/19 01:00:00 AM 03/16/19 04:20:00 AM

03/05/19 12:00:00 PM 03/05/19 02:40:00 PM 03/05/19 05:20:00 PM 03/05/19 08:00:00 PM 03/05/19 10:40:00 PM 03/06/19 01:20:00 AM 03/06/19 04:00:00 AM 03/06/19 06:40:00 AM 03/06/19 09:20:00 AM 03/06/19 12:00:00 PM 03/06/19 02:40:00 PM 03/06/19 05:20:00 PM 03/06/19 08:00:00 PM 03/06/19 10:40:00 PM 03/07/19 01:20:00 AM 03/07/19 04:00:00 AM 03/07/19 06:40:00 AM 03/07/19 09:20:00 AM 03/07/19 12:00:00 PM 03/07/19 02:40:00 PM 03/07/19 05:20:00 PM 03/07/19 08:00:00 PM 03/07/19 10:40:00 PM 03/08/19 01:20:00 AM 03/08/19 04:00:00 AM 03/08/19 06:40:00 AM 03/08/19 09:20:00 AM 03/08/19 12:00:00 PM 03/08/19 02:40:00 PM 03/08/19 05:20:00 PM 03/08/19 08:00:00 PM 03/08/19 10:40:00 PM 03/09/19 01:20:00 AM 03/09/19 04:00:00 AM 03/09/19 06:40:00 AM 03/09/19 09:20:00 AM 03/09/19 12:00:00 PM 03/09/19 02:40:00 PM 03/09/19 05:20:00 PM 03/09/19 08:00:00 PM 03/09/19 10:40:00 PM 03/10/19 01:20:00 AM 03/10/19 04:00:00 AM 03/10/19 06:40:00 AM 03/10/19 09:20:00 AM 03/10/19 12:00:00 PM 03/10/19 02:40:00 PM 03/10/19 05:20:00 PM 03/10/19 08:00:00 PM 03/10/19 10:40:00 PM 03/11/19 01:20:00 AM 03/11/19 04:00:00 AM 03/11/19 06:40:00 AM 03/11/19 09:20:00 AM 03/11/19 12:00:00 PM 03/11/19 02:40:00 PM 03/11/19 05:20:00 PM 03/11/19 08:00:00 PM 03/11/19 10:40:00 PM 03/12/19 01:20:00 AM 03/12/19 04:00:00 AM 03/12/19 06:40:00 AM 03/12/19 09:20:00 AM 03/12/19 12:00:00 PM 03/12/19 02:40:00 PM 03/12/19 05:20:00 PM 03/12/19 08:00:00 PM 03/12/19 10:40:00 PM 03/13/19 01:20:00 AM 03/13/19 04:00:00 AM 03/13/19 06:40:00 AM 03/13/19 09:20:00 AM 03/13/19 12:00:00 PM 03/13/19 02:40:00 PM 03/13/19 05:20:00 PM

Fig 25: Reduced heat loss due to infiltration in class 254. Source: Authors.

Comparison

Improvement

(mi) Graph 24: Improvment in room 277 after adding insulation and improved heating schedule. Source: Authors.

Measured Measured

Graph 23: Optimisation of class 254 after using the smart ventilation control system. Source: Authors.

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Improved

HOBO-3

5.4 Cluster : Newest (2010)

Activity

• Orientation : South • Class Surveyed : 171,172 • Class Measured : 171,172,272,371,372,373,374

• • • • •

Construction

Figure 26: Newest Cluster. Source: Design builder simulations

Design Builder Parameters Modeling • The first three levels of the newest part of the school has double façade. To model that we defined a narrow zone with 95 percent of glass and we defined interior windows for inside. General • • • • • •

• External Walls: Brickwork air thermolite and uf insulation with R-value of 4.9 • Pitched roof: Project pitched roof • Internal Partitions: Internal wall sub-surface construction • Internal Thermal Mass: Project thermal mass with zone capacitance of 3 • To simulate mechanical ventilation with heat recovery system we defined 20 percent of the needed ventilation in Airtightness component. • We defined a Model infiltration with constant rate of 1.4 ac/h Edu schedule. Openings • • • •

General Lighting: ON Normalized Power Density: 5 W/m2 – 100lux Luminaire Type: Suspended Working place height: 0.80m

Second skin: • Glazing Type – Sgl Clr 6mm • Window to Wall %: 95 percent Internal glazing: • Type: Fixed with and height • Glazing Type – Dbl LoE (e2=1) Clr 6mm/13 mm Arg • Type: Preferred height 1.5m, 30% glazing Third floor glazing: • Glazing Type – Dbl LoE (e2=1) Clr 6mm/13 mm Arg • Type: Fixed with and height

Graph 25: Simulation analysis of class 373 with false ceilings. Source: Authors.

• • • •

Window with (m) : 0.75 m Window height: 1.5 Window spacing: 1.25 Shading: NO

LIGHTING • • • •

General Lighting: ON Normalized Power Density: 5 W/m2 – 100lux Luminaire Type: Suspended Working place height: 0.80m

5.4.1 Improvement 1 As part of the improvement, class 373 was simulated with a false ceiling. As can be seen in graph 25, this affects the class temperature significantly. Since the class is located at South, it receives the most sun. Therefore, sun shading can be implemented in this class in order to prevent the sun’s heat from penetration in summer. In winter, the temperature is within the acceptable range most of the time.

HVAC • All is off The simulated temperature values from the cluster model in design builder was compared with the temperature readings of Hobo from 5th March till 10th March. The above mentioned settings and schedules were used to achieve a correlation for room 171,172,373, more then .75 as shown in appendix B. However, it is to be noted that 373 does not have the value above .75 but since the trend is similar we can still consider the model for further use in simulations.

Conclusion

Nowadays, with the education system in different countries, students spend most of their time in the classrooms. As the thermal comfort in classrooms can significantly affect the students’ learning process and their health and comfort, it is vital to provide a comfortable environment for the students. Taken as a case study, classrooms in the Stanislascollege Westplantsoen, located in Westplantsoen 71 Delft, were investigate to analyse their thermal comfort. The following research question mentioned in section 1of the report of whether the classrooms in the Stanislascollege Westplantsoen thermally comfortable in conjuction with influence of location of the classroom , causes of discomfort and which part needs the maximum intervention can be illustrated as below: • The classrooms exhibited discomfort with respect to the user perception.

• Adding more insulation to the facade of older parts of the school. • Smart control of the ventilation grills according to the indoor temperature. • Adding foil layers behind the radiators to reflect the heat back into the room. • Replacing the radiators in the old parts with thermostatic radiators • Colors can have psychological influence on the thermal perception. Classes can be painted with warm colors so that it manipulates how the students feel during the class. However, in the long term, this is not practical but it wouldn’t create any problems since the classes are not too long.

• The data loggers supported most of the classrooms with discomfort mentioned by the users.

• Changing the class furniture placement during summer and winter in order to prevent the draught happening in the middle of the class, causing the feeling cold.

• Among the clusters of older, newer, newest, we cannot distinguish which one is worse.

Scope of improvement

• The newest part need the maximum improvement, specially the classrooms on the third floor. It can also be concluded that the with respect to the causes of the discomfort , some improvements as suggested in section 5 can be helpful in improving the classrooms. Some of the improvements can be concluded as:

• Including male, female, age factor into the analysis. • Taking surveys and measurements four times a year in different conditions. • The survey could be more graphical and general. • More data loggers onto different surface of the classrooms.

• Giving instructions and explanations about different parts of the school, so that students and • Detailed building schedule for simulation the teachers know the school comprises different modeling parts and each parts needs a different behavior. In this order, students now how to dress/behave • Discussion with building manager to get accurate properly according to the classes they have. schedule for each classroom. • Adjusting the heating set point considering the floor level and orientation.

• This could help in specific simulations to give accurate suggestions.

References

Boerstra, A. C., van Hoof, J., & Van Weele, A. M. (2015). A new hybrid thermal comfort guideline for the Netherlands: background and development. Architectural Science Review, 58(1), 24-34. Dijken, F. V., Bronswijk, J. V., & Sundell, J. (2005). Indoor environment in Dutch primary schools and health of the pupils. Proceedings of indoor air, 2005, 623-7. EN 15251 (2007) Golshan, M., Thoen, H., & Zeiler, W. (2018). Dutch sustainable schools towards energy positive. Journal of Building Engineering, 19, 161-171. Hamzah, B., Gou, Z., Mulyadi, R., & Amin, S. (2018). Thermal comfort analyses of secondary school students in the tropics. Buildings, 8(4), 56. ISO 74 (2004) Kingma, B., & van Marken Lichtenbelt, W. (2015). Energy consumption in buildings and female thermal demand. Nature climate change, 5(12), 1054. NEN 7730 (2005) Nicol, F., Humphreys, M., & Roaf, S. (2012). Adaptive thermal comfort: principles and practice. Routledge. ter Mors, S., Hensen, J. L., Loomans, M. G., & Boerstra, A. C. (2011). Adaptive thermal comfort in primary school classrooms: Creating and validating PMV-based comfort charts. Building and Environment, 46(12), 2454-2461. Frisse scholen (n.d.). Retrieved from http://www. r vo.nl/onderwerpen/duurzaam-ondernemen/ gebouwen/frisse-scholen?gclid=CKmXl7Hi7scCFV W7GwodAtYCBg

Appendix

Appendix A

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03/05/19 12:00:00 PM 03/05/19 04:30:00 PM 03/05/19 09:00:00 PM 03/06/19 01:30:00 AM 03/06/19 06:00:00 AM 03/06/19 10:30:00 AM 03/06/19 03:00:00 PM 03/06/19 07:30:00 PM 03/07/19 12:00:00 AM 03/07/19 04:30:00 AM 03/07/19 09:00:00 AM 03/07/19 01:30:00 PM 03/07/19 06:00:00 PM 03/07/19 10:30:00 PM 03/08/19 03:00:00 AM 03/08/19 07:30:00 AM 03/08/19 12:00:00 PM 03/08/19 04:30:00 PM 03/08/19 09:00:00 PM 03/09/19 01:30:00 AM 03/09/19 06:00:00 AM 03/09/19 10:30:00 AM 03/09/19 03:00:00 PM 03/09/19 07:30:00 PM 03/10/19 12:00:00 AM 03/10/19 04:30:00 AM 03/10/19 09:00:00 AM 03/10/19 01:30:00 PM 03/10/19 06:00:00 PM 03/10/19 10:30:00 PM 03/11/19 03:00:00 AM 03/11/19 07:30:00 AM 03/11/19 12:00:00 PM 03/11/19 04:30:00 PM 03/11/19 09:00:00 PM 03/12/19 01:30:00 AM 03/12/19 06:00:00 AM 03/12/19 10:30:00 AM 03/12/19 03:00:00 PM 03/12/19 07:30:00 PM 03/13/19 12:00:00 AM 03/13/19 04:30:00 AM 03/13/19 09:00:00 AM 03/13/19 01:30:00 PM 03/13/19 06:00:00 PM 03/13/19 10:30:00 PM 03/14/19 03:00:00 AM 03/14/19 07:30:00 AM 03/14/19 12:00:00 PM 03/14/19 04:30:00 PM 03/14/19 09:00:00 PM 03/15/19 01:30:00 AM 03/15/19 06:00:00 AM 03/15/19 10:30:00 AM 03/15/19 03:00:00 PM 03/15/19 07:30:00 PM 03/16/19 12:00:00 AM 03/16/19 04:30:00 AM 03/16/19 09:00:00 AM 03/16/19 01:30:00 PM 03/16/19 06:00:00 PM 03/16/19 10:30:00 PM 03/17/19 03:00:00 AM 03/17/19 07:30:00 AM 03/17/19 12:00:00 PM 03/17/19 04:30:00 PM 03/17/19 09:00:00 PM 03/18/19 01:30:00 AM 03/18/19 06:00:00 AM 03/18/19 10:30:00 AM 03/18/19 03:00:00 PM 03/18/19 07:30:00 PM 03/19/19 12:00:00 AM

Appendix B

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Simulated (si)

Graph 1B: Correlation between measured and simulated data for class 113. Source: Authors.

Graph 2B: Correlation between measured and simulated data for class 213. Source: Authors.

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Graph3B: Correlation between measured and simulated data for class 254. Source: Authors.

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15.0

10.0

Graph 4B: Correlation between measured and simulated data for class 277. Source: Authors. Measured (mi) HOBO-3

Measured Class 171

Measured Class 172

Simulated (si)

30.000

Class 171

25.000

20.000

15.000

10.000

Simulated

Graph 5B: Correlation between measured and simulated data for class 171. Source: Authors.

Class 172

Simulated

Graph 6B: Correlation between measured and simulated data for class 172. Source: Authors.

03/05/19 12:00:00 PM 03/05/19 02:30:00 PM 03/05/19 05:00:00 PM 03/05/19 07:30:00 PM 03/05/19 10:00:00 PM 03/06/19 12:30:00 AM 03/06/19 03:00:00 AM 03/06/19 05:30:00 AM 03/06/19 08:00:00 AM 03/06/19 10:30:00 AM 03/06/19 01:00:00 PM 03/06/19 03:30:00 PM 03/06/19 06:00:00 PM 03/06/19 08:30:00 PM 03/06/19 11:00:00 PM 03/07/19 01:30:00 AM 03/07/19 04:00:00 AM 03/07/19 06:30:00 AM 03/07/19 09:00:00 AM 03/07/19 11:30:00 AM 03/07/19 02:00:00 PM 03/07/19 04:30:00 PM 03/07/19 07:00:00 PM 03/07/19 09:30:00 PM 03/08/19 12:00:00 AM 03/08/19 02:30:00 AM 03/08/19 05:00:00 AM 03/08/19 07:30:00 AM 03/08/19 10:00:00 AM 03/08/19 12:30:00 PM 03/08/19 03:00:00 PM 03/08/19 05:30:00 PM 03/08/19 08:00:00 PM 03/08/19 10:30:00 PM 03/09/19 01:00:00 AM 03/09/19 03:30:00 AM 03/09/19 06:00:00 AM 03/09/19 08:30:00 AM 03/09/19 11:00:00 AM 03/09/19 01:30:00 PM 03/09/19 04:00:00 PM 03/09/19 06:30:00 PM 03/09/19 09:00:00 PM 03/09/19 11:30:00 PM 03/10/19 02:00:00 AM 03/10/19 04:30:00 AM 03/10/19 07:00:00 AM 03/10/19 09:30:00 AM 03/10/19 12:00:00 PM 03/10/19 02:30:00 PM 03/10/19 05:00:00 PM 03/10/19 07:30:00 PM 03/10/19 10:00:00 PM

Class 373

30.000

25.000

20.000

15.000

10.000

Graph 7B: Correlation between measured and simulated data for class 373. Source: Authors. Measured class 373 Simulated With false ceiling