Distributed nonlinear model predictive control for building energy systems: An ALADIN implementation study

The implementation of sophisticated control strategies for building energy systems is crucial for improving energy efficiency and occupant comfort. While nonlinear model predictive control offers promising benefits, its application to large-scale building systems remains challenging due to computational complexity and system coupling. This work presents a comprehensive study of Nonlinear Distributed Model Predictive Control (NDMPC) implementation for building energy systems, comparing Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithms alongside different modeling approaches. We examine a multi-zone heating system with thermal storage and multiple producers, investigating both Ordinary Differential Equation (ODE)-based and Artificial Neural Network (ANN) based modeling strategies. Through systematic parameter tuning using Bayesian optimization and closed-loop scaling analysis with up to 40 thermal zones, we demonstrate that ALADIN-based NDMPC can achieve performance comparable to centralized model predictive control, showing greater robustness to parameter variations than ADMM. Our results reveal that ANN-based models effectively mitigate distributed integration errors and significantly reduce computation time compared to ODE-based approaches. Detailed computational profiling identifies specific bottlenecks in different NDMPC components. These findings advance the practical implementation of NDMPC in building energy systems, offering concrete strategies for modeling choices, parameter tuning, and system architecture design. • Nonlinear model predictive control for buildings with multivariate coupling. • ALADIN comes close to performance of central MPC while ADMM fails. • ALADIN is less sensitive to parameter and scaling variations than ADMM. • Neural Network based models reduce computation time compared to ODE-based approaches. • DMPC with ALADIN is faster than central MPC starting at 10 thermal zones.

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Publication Details

Journal
Energy and AI
Published
2025-06-19
DOI
https://doi.org/10.1016/j.egyai.2025.100536
Citations
7
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
8.50
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article

Distributed nonlinear model predictive control for building energy systems: An ALADIN implementation study

Dirk Müller, Steffen Eser, Phillip Stoffel, Ben Spoek et al.
7 citations
Energy and AI
Advanced Control Systems Optimization
8.50
article

Distributed nonlinear model predictive control for building energy systems: An ALADIN implementation study

Dirk Müller, Steffen Eser, Phillip Stoffel, Ben Spoek, Augustinus Schütz
article en
7 citations

Abstract

The implementation of sophisticated control strategies for building energy systems is crucial for improving energy efficiency and occupant comfort. While nonlinear model predictive control offers promising benefits, its application to large-scale building systems remains challenging due to computational complexity and system coupling. This work presents a comprehensive study of Nonlinear Distributed Model Predictive Control (NDMPC) implementation for building energy systems, comparing Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithms alongside different modeling approaches. We examine a multi-zone heating system with thermal storage and multiple producers, investigating both Ordinary Differential Equation (ODE)-based and Artificial Neural Network (ANN) based modeling strategies. Through systematic parameter tuning using Bayesian optimization and closed-loop scaling analysis with up to 40 thermal zones, we demonstrate that ALADIN-based NDMPC can achieve performance comparable to centralized model predictive control, showing greater robustness to parameter variations than ADMM. Our results reveal that ANN-based models effectively mitigate distributed integration errors and significantly reduce computation time compared to ODE-based approaches. Detailed computational profiling identifies specific bottlenecks in different NDMPC components. These findings advance the practical implementation of NDMPC in building energy systems, offering concrete strategies for modeling choices, parameter tuning, and system architecture design. • Nonlinear model predictive control for buildings with multivariate coupling. • ALADIN comes close to performance of central MPC while ADMM fails. • ALADIN is less sensitive to parameter and scaling variations than ADMM. • Neural Network based models reduce computation time compared to ODE-based approaches. • DMPC with ALADIN is faster than central MPC starting at 10 thermal zones.

Energy and AIVol. 21
RWTH Aachen University (DE)
Affordable and clean energy
Openalex Percentile: Top 2%
Advanced Control Systems Optimization
8.50
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