Abstract
There have been many attempts to reduce the uncertainty associated with renewable energy. This paper aims to contribute to the field by considering the building heating, ventilation and air conditioning (HVAC) system, which is treated as flexible load. The control of the indoor temperature in the building employing the locally generated renewable energy has been carried out with both model-based and data-driven approaches. Although numerous efforts have been made in the area of construction of a model for the building HVAC system, almost all models have shortcomings. A building model that is locally accurate is required to be able to predict the load consumption in the future. This will benefit both the utility companies and building managers to estimate the upcoming load shape, especially the peak amount and duration. We consider the case of a multi-zone building HVAC system, i.e., each zone has its own thermostat and can be set to a different temperature. Based on our need for a multi-zone model that is locally accurate, we resort to the method of system identification. In particular, we will employ the nuclear norm based subspace identification method.
