Data-driven identification of a thermal network in multi-zone building

TL;DR

This paper introduces a provable data-driven method to accurately identify the thermal interaction network of multi-zone buildings using temperature measurements, enhancing building control and demand response strategies.

Contribution

The paper presents a novel algorithm for reconstructing building thermal networks solely from temperature data, outperforming prior methods in complex scenarios.

Findings

Successfully reconstructed a 5-zone building network in EnergyPlus.

Accurately identified network topology under real-world conditions.

Outperformed previous methods in challenging scenarios.

Abstract

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.