In many educational institutions, heating is inefficient because systems operate based on pre-set schedules that do not adapt to the actual use of spaces. As a result, unnecessary energy costs arise, especially when rooms are unoccupied. The aim of this thesis was to develop an energy-efficient heating management system that uses IoT (Internet of Things) technologies to dynamically adjust temperature based on real needs. The system is based on LoRaWAN (Long Range Wide Area Network) technology, which enables remote communication with low energy consumption. It includes the MClimate Vicki smart thermostat, a Milesight occupancy sensor, a gateway, the TTN (The Things Network) platform, the Datacake platform, and a Python application for automatically retrieving schedules from the WTT (Wise Timetable) platform. The algorithm adjusts the target temperature based on occupancy and includes a time delay to preheat rooms in advance. A simulation in a real environment enabled the analysis of system performance, confirming that it successfully follows schedules, detects unexpected occupancy, and optimizes heating. A notable contribution of this thesis is also a simplified example of deploying the developed system in energy-inefficient public buildings in Slovenia, using data on floor area and energy consumption, with estimated savings based on case studies. The calculated payback period indicates that implementing such a system could significantly contribute to achieving national energy goals and reducing costs. Thus, the thesis presents a practical example of IoT use for energy optimization and provides a solid foundation for further development of smart building solutions.
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