## 5 Discussion  
Discussion about the obtained results (in previous Sections 4.1-4.6), concluded as following:  
* When the heating is off, evolution of temperatures directly depends on weather conditions, following a thermal inertia around 2-3 \({}^{\circ}\)C above outdoor temperatures. * When the heating is on, the building behaviour remains uniform in a range of approx. 6 \({}^{\circ}\)C. Thus, analysis of thermal evolution allows for the characterisation of working days (by monitoring their up-down trends which define sawtooth patterns), and identification of festive periods (by identifying significant drops in temperatures), even detecting anomalous situations (such as the opening of windows or doors, typical of public buildings). * Thermal performance exhibits a characteristic morphology (regarding occupancy) with: linear behaviour (before occupancy), upward behaviour (due to occupancy), downward behaviour (after occupancy, that stabilises at a level similar to the initial linear behaviour), downward behaviour (between occupancy schedule, related to absence of people), and several non-pattern behaviours (related to unpredictable situations, as presence of people out of their schedule, detected by increasing CO2 levels). * Analysis of time percentages when temperatures fulfil the recommended thresholds (between 17, 19, and 21 \({}^{\circ}\)C) shows that majority of monitored spaces (out of 17; 12 in November, 15 in December, 14 in January) comply with the directives (17-21 \({}^{\circ}\)C) for most of the time (+40% in November, +60% in December, +75% in January). This is not the case in February since all spaces (17 of 17) exceed 21 \({}^{\circ}\)C more than 45% of the time (the majority, 14 of 17, +60%). This excess (>21 \({}^{\circ}\)C) is lower in previous months (out of 17, +50% of time: 8 in November, and only 2 in December and January). The previous results quantify the potential savings from heating management (turning it off at the appropriate times) by estimating it as around EUR 500 in November (29.9 h and 7.92 MWh), around EUR 370 in December (18.6 h and 5.13 MWh), around EUR 330 in January (16.5 h and 4.49 MWh), and around EUR 1000 in February (92.8 h and 25.08 MWh). These estimations (indicative in themselves) are very significative extrapolated to every month and to all buildings on campus. This specific analysis, focused on temperature data, would require around 20 effective monitoring points. For this purpose, each sensor costs between EUR 100 and EUR 200, with a receiver station priced at approximately EUR 500. While the broader monitoring framework of the building incorporates a much larger network of sensors to gather additional data, the cost of the subset needed for temperature monitoring remains modest. This demonstrates that substantial Return of Investment (ROI) can be achieved with a targeted and cost-efficient deployment of monitoring IoT technologies. The results underscore the viability of such systems, for reducing energy consumption and associated costs, and also for contributing to broader sustainability challenges with minimal upfront investment in temperature-specific devices. These results align with prior research on energy efficiency in Mediterranean climates. For instance, recent studies have demonstrated the effectiveness of integrating passive design strategies (such as double facades and photovoltaic systems) to reduce energy demand while maintaining thermal comfort [20]. Similarly, recent studies highlight the key potential of adaptive thermal comfort approaches, which consider behavioural adaptations to optimise energy use in various climatic conditions [26]. This work builds on these findings by incorporating IoT technologies to dynamically monitor and manage building conditions, allowing for real-time adaptability that enhances both energy savings and occupant satisfaction. The obtained results, although optimised for Mediterranean climates, have implications for broader climatic contexts.