A model predictive control optimization environment for real-time commercial building application

Abstract

A model predictive control (MPC) environment is described. The environment integrates Matlab and EnergyPlus with a modified particle swarm optimizer to predict optimal building control strategies. A supporting framework is described which couples the environment to a building automation system, allowing real-time optimization considering operator overrides and updated weather forecasts. Challenges unique to integration with EnergyPlus for real-time optimization are discussed. Application of the environment is demonstrated in two simulation cases. First, the environment is used to determine hourly cooling set points minimizing daily energy cost for EnergyPlus's Benchmark Large Office building. Results suggest 5% cost savings during the study period. Second, the environment is used to determine hourly supply water temperature and circulator availability that minimize daily energy consumption for a small office building having a thermally activated building system (TABS). Compared to the base case, energy savings up to 54% are reported, with often improved occupant comfort.

Keywords:

• particle swarm optimization
• model predictive control
• software
• EnergyPlus
• energy simulation

Acknowledgements

Portions of his work have been sponsored through a research contract with Clean Urban Energy, Inc., for which the authors would like to express their sincere gratitude. Moreover, G.P. Henze discloses his role as technology advisor and co-founder of Clean Urban Energy, Inc. Additional funding for TABS control research was provided under contract with ITG Dresden.