Building energy model reduction for model predictive control using OpenStudio

Model-based predictive control for buildings is an active area of research. Significant effort has been placed on developing accurate and computationally efficient reduced-order models that can be implemented in predictive controllers. During a building´s design and construction process, detailed building models are often created by experienced building modelers. These models are often too complex to be directly implemented in control applications. Reducing these models to low-order models can be time-consuming and require additional skills beyond those possessed by building modelers. In this paper we demonstrate simple reduction of building models using the OpenStudio analysis framework in a script-based environment. OpenStudio is a cross-platform tool for modeling and analysis of building energy systems. A reduced-order model is created for a simple building and an economic-based model predictive controller is used to minimize summertime cooling costs in an electricity market with real-time pricing.