Dynamic thermal demand analysis of residential buildings based on IoT air conditioner

With the development of communication technology and artificial intelligence, the application of IoT (Internet of Things) has driven the development of smart cities. Real-time and non-intrusive IoT air-conditioning data as a supporting source provides new possibilities for studying dynamic heat demand in residential buildings. However, the data structure of IoT is very different from that of traditional sources. Therefore, this study conducts a dynamic thermal demand analysis of residential buildings at a regional scale based on IoT air conditioners. Firstly, the data preprocessing paradigm for the IoT air conditioner was constructed. Next, the change of dynamic thermal demand was comparatively analyzed through the coupling regulation of the air conditioner set temperature and wind velocity, as well as the frequency adjustment of active functions in two functional rooms and three demand groups. There were two further findings, by evaluating the PMV fluctuation performance under different operating conditions, the retaliatory and economic thermal demand in different groups were found to exist. Meanwhile, another finding was that there were seasonal adaptive adjustments and delayed compensatory adjustments for air conditioners. Moreover, the energy-saving potential of air conditioner operation under energy-saving awareness was quantified. From lower to upper class, 0.90%, 0.94%, and 0.38% of the accumulative time spent by bedroom users have thermal demand energy-saving potential of 75%, and 0.31%, 0.44%, and 0.33% have thermal demand energy-saving potential of 6%.