Reduced-Order Residential Home Modeling for Model Predictive Control

Wesley J. Cole, Kody M. Powell, Elaine T. Hale, Thomas F. Edgar

Research output: Contribution to journalArticlepeer-review

75 Scopus Citations


Building simulation software packages such as EnergyPlus are useful energy modeling tools. These software packages, however, are often not amenable to model-based control due to model complexity or difficulties connecting control algorithms with the software. We present a method for automatically generating input/output data from an EnergyPlus residential home model using the OpenStudio software suite. These input/output data are used to create a simple reduced-order model that can be evaluated in fractions of a second. The reduced-order model is implemented in a model predictive controller to minimize the home's electricity costs during summer months in Austin, Texas, USA. The controller optimally precools the home in the morning and turns down or off the air conditioning system in the afternoon. For this example, electricity prices were taken from actual market prices in the Austin area. The optimal precooling strategy given by the model predictive controller reduces peak energy consumption from the air conditioning unit by an average of 70% and reduces operating costs by 60%. Precooling, however, consumes more total energy versus not precooling. Reducing peak energy consumption by 1 kWh results, on average, in an increase of 0.63 kWh in overall energy consumption.

Original languageAmerican English
Pages (from-to)69-77
Number of pages9
JournalEnergy and Buildings
StatePublished - May 2014

NREL Publication Number

  • NREL/JA-5500-61461


  • Building energy simulation
  • EnergyPlus
  • Model predictive control
  • Model reduction
  • OpenStudio
  • Precooling
  • Thermal energy storage


Dive into the research topics of 'Reduced-Order Residential Home Modeling for Model Predictive Control'. Together they form a unique fingerprint.

Cite this