Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System
arXiv:2407.13308 · doi:10.1109/ISGTEUROPE62998.2024.10863763
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.
arXiv comment5 pages, 4 figures. To be published in 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) proceedings
References in corpus (5)
- XGBoost: A Scalable Tree Boosting System
- Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
- Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
- Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
- Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System