Privacy-Preserved Aggregate Thermal Dynamic Model of Buildings

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
View graph of relations

Author(s)

  • Zeyin Hou
  • Shuai Lu
  • Yijun Xu
  • Haifeng Qiu
  • Wei Gu
  • Shixing Ding

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)5653-5664
Number of pages12
Journal / PublicationIEEE Transactions on Smart Grid
Volume15
Issue number6
Online published28 Jun 2024
Publication statusPublished - Nov 2024

Abstract

The thermal inertia of buildings brings considerable flexibility to the heating and cooling load, which is known to be a promising demand response resource. The aggregate model that can describe the thermal dynamics of the building cluster is an important interference for energy systems to exploit its intrinsic thermal inertia. However, the private information of users, such as the indoor temperature and heating/cooling power, needs to be collected in the parameter estimation procedure to obtain the aggregate model, causing severe privacy concerns. In light of this, we propose a novel privacy-preserved parameter estimation approach to infer the aggregate model for the thermal dynamics of the building cluster for the first time. Using it, the parameters of the aggregate thermal dynamic model (ATDM) can be obtained by the load aggregator without accessing the individual’s privacy information. More specifically, this method not only exploits the block coordinate descent (BCD) method to resolve its non-convexity in the estimation but investigates the transformation-based encryption (TE) and the secure aggregation protocol (SAP) methods to realize privacy-preserved computation. Its capability of preserving privacy is also theoretically proven. Finally, simulation results using real-world data demonstrate the accuracy and privacy-preserved performance of our proposed method. © 2024 IEEE.

Research Area(s)

  • Aggregate model, buildings, demand response, nonconvex parameter estimation, privacy analysis, privacy-preserved computation

Citation Format(s)

Privacy-Preserved Aggregate Thermal Dynamic Model of Buildings. / Hou, Zeyin; Lu, Shuai; Xu, Yijun et al.
In: IEEE Transactions on Smart Grid, Vol. 15, No. 6, 11.2024, p. 5653-5664.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review