Model Predictive Control For Energy Efficiency of Buildings Using Active Thermal Mass

Tech ID: 31944 / UC Case 2020-095-0


Current systems for managing thermal behavior of buildings are limited by complex higher-processing models that often require a cloud connection. These technologies improve energy optimization by using various sensory inputs to treat and control “thermal zones”. Consequently, managing a building via feedback from multiple thermal units increases the number of modelling components to be considered and the amount of thermal differentiation throughout the structure. Controlling indoor climatic conditions using this approach is, therefore, error-prone, inefficient, and expensive.  


Researchers at the University of California, Santa Barbara have introduced the principle of active thermal mass in a model predictive control approach to simplify and improve energy efficiency of buildings. Using active thermal mass as an actuator allows this system to boost energy and cost savings by streamlining thermal management with simple, data-driven environmental models. In addition, controlling structures with a singular thermal unit eliminates the need for complex higher-processing models by reducing the number of variables considered when generating an optimal model for a building management system. 


  • Reduced heating and cooling costs
  • Increased heating and cooling efficiency
  • Simplified thermal management


  • Smart buildings (residential and commercial)
  • Energy management systems


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Other Information


model predictive control, thermal mass, BMS, building management system, energy efficiency, smart building

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