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.
|Title of host publication
|2018 IEEE Conference on Decision and Control, CDC 2018
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jul 2 2018
|57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018 → Dec 19 2018
|Proceedings of the IEEE Conference on Decision and Control
|57th IEEE Conference on Decision and Control, CDC 2018
|12/17/18 → 12/19/18
Bibliographical noteFunding Information:
The authors H. Doddi, S. Talukdar, and M. V. Salapaka acknowledge the support of ARPA-E for supporting this research through the project titled ‘A Robust Distributed Framework for Flexible Power Grids’ via grant no. DEAR0000701 and Xcel Energy’s Renewable Development Fund. D. Deka acknowledges the support of funding from the Center for Non-Linear Studies at LANL and the Grid Modernization Initiative of the U.S. Department of Energy’s Office of Electricity.
© 2018 IEEE.