AS_03_2020

tecnica 92 Aprile 2020 Automazione e Strumentazione CONTROLLO been assigned to ‘ND’ (not defined). As for the temperature-side analysis, the general behaviour of the CO2 level con- trol has shown poor per- formances over many rooms, with a frequency of excessive CO2 levels around 50% and CO2 level peaks almost close to 100 PPMx100. Such performance highlights a reasonable margin for improvement. 3. Model development The model developed in this section lays the foundation of the deve- lopment and test of advanced control strategies . Through the development and validation of the building dynamic models, a veri- fied simulation test is available for any control performance tests. To develop such models, multiple information sources were used. First, real data was collected from the building on the following measurements : air temperature for each room, CO2 levels for each room, fan coil system command for each room, air recircu- lation command for each room, water temperature of the fan coil circuit, external air temperature and recirculated air temperature. Then, by identifying the correct model of the installed fan coil devices, it was possible to retrieve the specific data sheet about its thermal behavior. From the project design parameters, it was pos- sible to retrieve the recirculated air mass flow imposed by the air recirculation system for each room. In particular, the air recircula- tion system is designed to recycle 2 volumes of air each hour for each room at maximum capacity. Finally, the building structural parameters declared in the building project gave an insight over the expected thermal behavior of the building itself. Then, the model tuning corrected these initial parameters according to the measured data in order to fit the measured thermal behaviour of the building. A model describing the behaviour of a single building room is developed starting from the well-known relationships already presented in literature [6] [7] . Such model is then extended to the behavior of any other building room just by re-parameterization according to the structure of each room. The dynamic model was adopted to describe the behaviour of each room temperature and CO2 levels and is presented in the following equation: In such equation, T z represents the room air temperature, T w represents the room walls temperature and V CO2 represents the volume of CO2 in the room. The room air temperature state equation presents four terms: the thermal power injected by the fan coil devices, the convective heat exchange with the room walls, the thermal power produced by the people in the room and the thermal power resulting from the air recirculation characte- rized by a temperature T air . The room walls temperature state equation presents only two convective heat exchange terms, one with the room air and one with the external air, thus neglec- ting the solar radiation impact for practical purposes. Finally, the CO2 volume state equation envisages the CO2 production rate coming from the people occupying the room and the CO2 removal action resulting from the air recirculation. The presented model has been tuned over a set of real measured data. Then, the model has been validated over a second set of real measured data and is presented in υ figure 2 . An appre- ciable level of fitting over the air temperature and the CO2 level dynamics is shown, thus confirming the capability of the deve- loped model in representing the thermal and CO2 dynamics of a building room. 4. Advanced control techniques The advanced control strategies which were found to be most suitable are presented in detail, together with a comparative performance analysis with respect to the baseline control stra- tegy currently implemented in Building 25. These control strategies strongly rely on a measurement introdu- ced thanks to the newly implemented IoT sensor network, i.e. the people occupancy measurement. The current control stra- Figure 2 - Model validation