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.