@inproceedings {pub5709,
	title = {Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System},
	author = {Jens Engel AND Thomas Schmitt AND Tobias Rodemann AND J{\"u}rgen Adamy},
	year = {2024},
	abstract = {A major challenge in the development of Model
Predictive Control (MPC)-based energy management systems
(EMSs) for buildings is the availability of an accurate model.
One approach to address this is to augment an existing gray-box
model with data-driven residual estimators. The efficacy of such
estimators, and hence the performance of the EMS, relies on the
availability of sufficient and suitable training data. In this work, we
evaluate how different data availability scenarios affect estimator
and controller performance. To do this, we perform software-in-
the-loop (SiL) simulation with a physics-based digital twin using
real measurement data. Simulation results show that acceptable
estimation and control performance can already be achieved with
limited available data, and we confirm that leveraging historical
data for pretraining boosts efficacy.},
	publisher = {IEEE},
	booktitle = { IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) 2024}
}