An Internet of Things Compliant Model Identification Methodology for Smart Buildings

The token-based scheduling system (TBSS) provides significant scalability and reasonable performance. However, during implementation, it requires solving the MPC in a distributed fashion. The MPC assumes a reasonable model of the zone, estimate on the disturbances, and uses an optimization routine to compute the future control moves that optimize the energy consumption. As building models are complex and nonlinear, it depends on numerous variables intrinsic and external to a building that are time-varying, the model needs to be learned online.

In a TBSS, the network bandwidth emerges as a key concern as sensor readings have to be transmitted at regular intervals. As the scale of the building increases, techniques to reduce network bandwidth without affecting model fidelity are required. This requires optimal utilization of the network bandwidth and allocation of sensors. The problem is compounded by the presence of data losses. Consequently, model identification methods that can reduce network bandwidth and are robust to packet loss are required for HVAC systems. This can be achieved by showing that system identification can be done with more sparse measurements. Therefore, the user does not need to transmit intense data sets through the network to identify the zone model. At the same time, since model identification is done in real time, an efficient system identification algorithm is needed to identify many different sparse zone model at the same time with the available data.