## 4 Results
### Analysis of Thermal Evolution  
To begin the analysis, a general comparison of temperature trends is presented. Figure 3 shows the average temperature, calculated as the mean of the indoor temperatures in each monitored space (weekly and monthly), offering the following interesting insights:  
* October temperatures (solid blue curve) exceed 21 \({}^{\circ}\)C. This can be attributed to good weather conditions during this month (solid black line) [52], which can be quantified as an average thermal inertia of approximately 2-3 \({}^{\circ}\)C above outdoor temperatures;
* With these favourable temperatures, heating is not activated. Subsequently, Section 4.3 compares October (without heating) with the other months (with heating) to analyse the climate behaviour of the building. Furthermore, October (without heating) is chosen in Section 4.4 to analyse the behaviour (net, without heating influence) of indoor temperatures, their evolution, and their morphological characteristics;
* During the other months with heating (red, green, and yellow curves), compared to October without heating (blue curve), the behaviour is more uniform remaining within an approximate range of 6 \({}^{\circ}\)C (between 17 \({}^{\circ}\)C and 23 \({}^{\circ}\)C, the thresholds are marked by the dashed black lines). Additionally, in all cases, each day of the week follows a rise-and-fall pattern, which, weekly, defines a sawtooth behaviour. To understand this phenomenon, is key to study the thermal inertia of the building, see Section 4.2;
* During the months with heating, the drops in average temperatures reveal periods where heating is off. As outlined in black boxes (and analysed in detail in Section 4.2), these correspond to holidays periods (green curve), specifically in week 1 (6-8 December) and week 4 (23-31 December);
* Detailing months with heating, the average temperatures in November (red curve) are higher (by 1-2 \({}^{\circ}\)C) compared to December and January (green and yellow curves). As discussed in Section 4.2, this is due to better weather conditions in November;
* Several behaviours related to occupancy stand out (as analysed in Section 4.4). Week 1 of January (yellow curve), shows lower temperatures. This week is non-teaching but still a working period; thus, even though the heating is on, the thermal inertia must recover after many days without heating (since week 4 of December, green curve). This requires the entire week 1 in January to reach the average temperature range of the other months (from 17 \({}^{\circ}\)C). Additionally, January (yellow curve) shows the greatest variation between maximum and minimum temperatures. In this campus context, this is explained by the non-teaching period (exams season), which results in lower average occupancy and, consequently, daily temperature drops to lower minimums.  
These trends highlight that the proposed IoT-driven methodology, based on real-time monitoring of experimental measurements, pursues a double contribution:  
* to compare measurements of the current year with previous years to make experience-based decisions. In Mediterranean climates, the fall season usually alternates cold-dry with warm-wet periods; thus, the results of this work will serve as behaviour patterns on which to adjust pattern-based prediction models;to include new measurements (from season to season) into the IoT datasets to incorporate this knowledge into the prediction models and retraining them iteratively to obtain predictions adjusted to climate reality.