Comparative analysis of model predictive control performance using different building RC-models and state estimation techniques
Year: 2025
Type: article
Abstract: Model Predictive Control (MPC) applied to buildings often relies on gray-box controller models, such as RC network models, for which different orders can be considered. It is widely accepted that the prediction accuracy of RC-models increases with a higher order if properly identified. However, gray-box MPC performance can depend on the quality of the considered state estimator as well. Recent comparative analyses consider either different controller models or different state estimators. This work assesses multiple gray-box MPCs, ranging from a first- to a third-order RC-model, each combined with a Time-Varying Kalman Filter and a Moving Horizon Estimator and compares with using no state observer. System identification results show that tracking derivatives facilitates the challenging parameter estimation process. Therefore, a novel approach for system identification of RC-models is proposed by explicitly tracking model derivatives during training. The MPC benchmarking elucidates the necessity of both a good derivative-tracking controller model and a dedicated state estimator to guarantee good MPC performance.
Source: Energy and Buildings
Authors: Jaap Neven, Brida V. Mbuwir, Lieve Helsen
Institutions: Flemish Institute for Technological Research, KU Leuven
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,260
OpenAlex ID: W4416580786