Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications

2025/08/12 by Ninad Gaikwad, K. Shiva Shankar, Gaikwad, Ninad +7
Engineering · #Building Energy and Comfort Optimization #FOS: Electrical engineering #Integrated Energy Systems Optimization #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2508.09118

openalex publication_date 2025/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We need computationally efficient and accurate building thermal dynamics models for use in grid-edge applications. This work evaluates two grey-box approaches for modeling building thermal dynamics: RC-network models and structured regression models. For RC-network models, we compare parameter estimation methods including Nonlinear Least Squares, Batch Estimation, and Maximum Likelihood Estimation. We use the Almon Lag Structure with Linear Least Squares for estimating the structured regression models. The performance of these models and methods is evaluated on simulated house and commercial building data across three different simulation types.

Citations

• An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

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