## 4 Results
### Analysis of Thermal Morphology  
From the uniform pattern when the heating is on, this section analyses the thermal behaviour without heating. This led to an analysis of the morphology of temperature evolution. As an initial approach, the thermal behaviour is compared with itself on the same day of the week during a month. Based on a review of university lecture schedules, Tuesday has been identified as the most representative day for this study. Figure 9a shows the temperature evolution (from 00:00 to 24:00 h on the X-axis) over the four weeks of October: red (week 1, Tuesday 3 rd); orange (week 2, Tuesday 17 th, as 10 th was a holiday); blue (week 3, Tuesday 24 th); and green (week 4, Tuesday, 31 st). Although no general pattern emerges, several areas of temperature increase and decrease can be identified. To explain these trends, Figure 9b builds upon Figure 9a by adding vertical black lines following occupancy schedules (obtained from [54]). A horizontal band (at the top) indicates the building's opening hours (from 8 h to 21 h), while occupied hours are highlighted in grey. This study contributes to the conceptualisation of a morphological pattern, divided into following several key areas, as illustrated in Figure 9b:  
* Initial linear behaviour before the first grey band (start of classes at 9 h);
* Upward behaviour during grey bands (lectures schedule), due to occupancy;
* Downward behaviour after the last grey band (end of classes at 18 h), that stabilises at a level similar to the initial linear behaviour and start of the curve for the following day. The difference between initial and final daily temperatures is modulated by the average building temperature, which is conditioned by the weather conditions, as discussed in Sections 4.2 and 4.3;
* Downward behaviour between grey bands (can be multiple on a day regarding schedule), related to absence of people. This will be discussed in Section 4.6 by including the relationship with other key variables, as evolution of CO\({}_{2}\) levels;
* Several non-pattern behaviours (as downward level at the beginning of the day, previous of first classes), related to unpredictable situations: opening of windows or doors, presence of people out of their schedule, etc. This confirms the interest of understanding the complexity of the SBE, as contributed by this work. To complete this morphology study, the comparative thermal evolution is analysed along with their interrelationship with other study variables, such as CO\({}_{2}\) and occupancy. For example, Figure 10b shows the temperature in week 2, orange line extracted from Figure 10a, compared with the evolution of CO\({}_{2}\) (blue line, scaled in right Y-axis). The thermal increases-and-decreases can be explained following the rise-and-fall of CO\({}_{2}\) levels (blue line). Furthermore, these thermal increases-and-decreases and rise-and-fall of CO  
Figure 8: Analysis (detailed) of the heating influence: (**a**) Thermal evolution vs. heating; (**b**) Thermal evolution vs. heating vs. outdoor temperature. levels coincide with grey bands of occupancy. This confirms that, with occupancy (from 9 h to 13 h and 15 h to 18 h), shaded in yellow in Table 2, the temperature increases (orange line) and CO2 rises (blue line). This led to the use of IoT ecosystem to confirm the expected occupancy and to explore an interesting featured application as a detector of anomalous situations. An example of this potential use is shown in the temperature evolution of week 1, see the red curve in Figure 10a. Between 9 h and 13 h (where an increase would be expected) the temperatures remain linear. However, upon checking the timetable, see Table 2, it confirmed that classes from 9 h to 13 h had not yet started during that first week, meaning the space was empty. This demonstrates the absence of people, which justifies those temperatures remain linear. Abnormalities in the relationship between indoor temperature and occupancy arise due to two main factors: technical and human.