@article {pub-5606,
	title = {Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry},
	author = {Jens Engel AND Thomas Schmitt AND Tobias Rodemann AND J{\"u}rgen Adamy},
	year = {2024},
	abstract = {The increasing adoption of renewable energy sources (RESs) in public power grids has led to a demand for more intelligent
energy management systems (EMSs) in large-scale buildings. A common approach for controlling EMSs for buildings is
Model Predictive Control (MPC). For large-scale buildings, hierarchical MPC schemes have been proposed, offering the
advantage of scalability through problem decomposition into multiple layers. However, hierarchical schemes often suffer
from information mismatch due to information asymmetry between layers, leading to suboptimal control performance.
This issue is worsened by model errors inherent in the models underlying the MPC controllers. To address these challenges,
we propose a hierarchical MPC approach, which includes data-driven regression-based error compensation. Additionally,
to mitigate information mismatch, a one-iteration communication step is introduced between the hierarchical layers.
The proposed approach comprises two layers: an aggregator layer that controls overall energy flows of the building,
and a distributor layer that allocates thermal energy to individual temperature zones. The approach is evaluated using
a software-in-the-loop (SiL) simulation using a physics-based digital twin model of a multi-zone commercial building,
showing notable improvements in overall control performance.},
	publisher = {Elsevier},
	journal = {Applied Energy}
}