  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.
