Commercial buildings are becoming smart elements that can interact with the power grid, participating in grid-level operations, meeting occupant needs and economic objectives, while enhancing the resiliency of power systems. Advanced controllers, such as model predictive control and reinforcement learning, are ideal for these novel applications which require optimal performance under varying boundary conditions, which traditional rule-based controllers are incapable of. In order to implement model predictive and reinforcement learning controllers, one of the main requirements is to have a suitable building model. However, the extensive engineering time and effort required for model development, together with the inaccuracies produced when modeling a complex real building, have delayed the deployment of advanced controllers. To overcome these difficulties, the development of accurate and computationally efficient models with low complexity, extracted from building operational data, emerges as a potential solution. The extraction of simplified models, or reduced order models, from operational data has been a key area of concern for researchers during the last decades, but its application has often been limited to specific systems, buildings, or test cases. There is a need for an open-source and standardized methodology that would render reduced order models readily available to controller designers. This would, in turn, foster the development of advanced control strategies and their application to real building systems. In this paper, a model order reduction workflow is presented as part of the development of an advanced control test bed that allows the development of advanced control algorithms for building applications. Here, several reduced order models have been created using a model order reduction method called numerical subspace state space system identification. Finally, these models have been used in the implementation of model predictive control and reinforcement learning control agents, demonstrating and showing the capabilities and functionalities of the proposed model order reduction workflow.
