Data-driven identification of a thermal network in multi-zone building
For building control and demand response, this provides a provable topology identification method that works where previous approaches fail.
The paper presents a method to reconstruct the thermal zone interaction topology of a building from temperature measurements alone, demonstrating accurate reconstruction for a 5-zone office building in EnergyPlus under real-world conditions, outperforming prior methods.
System identification of smart buildings is necessary for their optimal control and application in demand response. The thermal response of a building around an operating point can be modeled using a network of interconnected resistors with capacitors at each node/zone called RC network. The development of the RC network involves two phases: obtaining the network topology, and estimating thermal resistances and capacitance's. In this article, we present a provable method to reconstruct the interaction topology of thermal zones of a building solely from temperature measurements. We demonstrate that our learning algorithm accurately reconstructs the interaction topology for a $5$ zone office building in EnergyPlus with real-world conditions. We show that our learning algorithm is able to recover the network structure in scenarios where prior research prove insufficient.