Date of Award
Master of Science (MS)
Civil, Environmental & Architectural Engineering
Gregor P. Henze
Matthew R. Morris
This thesis explores the viability of model predictive control strategies for a retrofit rooftop unit control solution developed by start-up technology provider Transformative Wave, called the CATALYST. This research has been conducted in conjunction with a research project funded by the Wells Fargo Innovation Incubator program, in collaboration with the National Renewable Energy Laboratory (NREL) to enhance the commercial retrofit rooftop unit control solution with optimum control development. The goal is to develop a simple, easily implementable model predictive controller to further reduce energy costs in commercial retail buildings. This thesis extends previously developed building performance simulation models and model predictive control tools to provide insight into the demands of model development and baseline optimum control results. Model development is approached by taking data provided only from the CATALYST controllers to estimate model parameters of a building and its HVAC systems sight unseen. Additional optimization tasks are evaluated to test the effectiveness of the building to improve electric grid integration and achieve carbon reductions. It was found that the MPC, within the simulation environment, was best able to reduce peak demand utility costs and to improve the building in terms of grid relationship but at the cost of increased energy consumption and carbon emissions. In terms of utility cost savings, the addition of model predictive control was able to save approximately $243 a month in utility demand charges at an increased cost of $8.42 a week resulting in total utility bill cost savings of $210 a month. This suggests an annual demand cost savings of 5% at a 2% energy cost increase.
Weskalnies, Paul, "Model Predictive Control for Retrofit Rooftop Unit Control" (2017). Civil Engineering Graduate Theses & Dissertations. 169.