Robust Parameter Estimation of Aggregate Thermal Dynamic Model: A Privacy-preserved Approach

An aggregate model that accurately quantifies buildings’ thermal flexibility is an essential interface for energy system operation. However, privacy concerns and measurement inaccuracies make obtaining an accurate aggregate model challenging. To address this, we propose a privacy-preserved robust parameter estimation approach to get the aggregate model of buildings. The proposed method ensures the accurate modeling of the aggregate thermal dynamics of buildings against outliers in measurements, without exposing any sensitive information from end users. The effectiveness of the proposed method is validated through numerical tests.