Dynamic mode decomposition for nonintrusive and robust model predictive control of residential heating systems

Obtaining accurate models for heating and building systems is crucial for prediction and control in the context of energy efficiency and demand response. Models should be both computationally and data efficient, as well as easy to implement. This paper therefore introduces a methodology for data-driven modeling and control of residential heating systems and buildings. Dynamic mode decomposition is used to fit a linear state-space model of the building and the heating system. It is shown that this procedure results in prediction accuracy that is akin to the literature on greybox models. In order to cope with the uncertainty around weather predictions, the state-space model is integrated in a robust linear model predictive control framework. The controller exhibits the required energy shifting behavior while only requiring a dataset size on the order of days. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a data-driven methodology for modeling and controlling residential heating systems and buildings. Dynamic mode decomposition is utilized to fit a linear state-space model, resulting in prediction accuracy similar to greybox models. To address weather prediction uncertainty, a robust linear model predictive control framework is used.