  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.
