  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) associated with its secure aggregation
protocol (SAP) techniques 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.
