Real-Time Energy Management for Building Systems back

Real-Time Energy Management (EM) systems can make commercial buildings more active participants in next-generation electricity markets because they enable prediction of HVAC electricity demands as a function of weather, occupancy trends, and comfort requirements and they enable the determination of load shedding potential for demand response.


Sponsored by the Department of Energy (DOE) Building Technologies Program, we have developed and tested novel optimization concepts for Real-Time EM.  In conjuction with BuildingIQ, we are studying energy savings potential in real commercial buildings such as the Advanced Photon Source (APS) building at Argonne (see Figure 1).  We are developing cost-effective measurement and verification (M&V) techniques using Gaussian Process (GP) modeling to determine energy savings and uncertainty levels. We also make use of moving horizon estimation and regularization techniques to infer the number of occupants using carbon dioxide and air flow measurements (see Figure 2) and we study trade-offs of comfort and energy savings using multi-objective optimization techniques to inform demand response tasks.

  • Yeonsook Heo (Cambridge University) [link]
  • Diane Graziano [link]
  • Michael Burkhart
  • BuildingIQ [link]
  1. Burkhart, M. C.; Heo, Y. and Zavala, V. M. Measurement and Verification of Building Systems Under Uncertain Data: A Gaussian Process Modeling Approach.  Under Review,  2013. [pdf]
  2. Zavala, V. M. Inference of Building Occupancy Signals Using Moving Horizon Estimation and Fourier Regularization.  Journal of Process Control, In Press,  2013. [pdf]
  3. Heo,Y.; Graziano, D.; Zavala, V.M.; Dickinson, P.; Kamrath, M. and Kirshenbaum, M.;  Cost-effective Measurement and
    Verification Method for Determining Energy Savings under Uncertainty.
    ASHRAE Annual Conference, 2013. [pdf]
  4. Zavala, V. M.  Real-Time Optimization Strategies for Building Systems. Ind.Eng.Chem.Res. 52(9), pp. 3137-3150, 2013. [pdf] [code]
  5. Heo, Y. and Zavala, V. M.  Gaussian Process Modeling for Measurement and Verification of Building Energy Savings.              Energy & Buildings, 53, pp.7-18, 2012. [pdf]
  6. Zavala, V.M. Real-Time Resolution of Conflicting Objectives in Building Energy Management: An Utopia-Tracking Approach. Simbuild, 2012. [pdf]
  7. Zavala, V. M., Skow, D., Celinski, T. and P. Dickinson. Techno-Economic Evaluation of a Next-Generation Building Energy Management System. Argonne National Laboratory, ANL/MCS-TM-313, 2011. [pdf]
  8. Zavala, V. M.; Constantinescu, E. M.; Krause, T.; and Anitescu, M.  On-Line Economic Optimization of Energy Systems Using Weather Forecast Information. Journal of Process Control, 19(10), pp. 1725-1736, 2009. [pdf]
  9. Zavala, V. M.;  Wang, J.; Leyffer,  S.; Constantinescu, E.M.; Anitescu, M. and Conzelmann, G.  Proactive Energy Management for Next-Generation Building Systems. SimBuild, 2010. [pdf]
  10. Zavala, V. M.;  Constantinescu, E.M. and Anitescu, M.  Economic Impacts of Advanced Weather Forecasting on Energy System Operations. IEEE PES Conference on Innovative Smart Grid Technologies, 2010. [pdf]
  1. Gaussian Process Modeling: Applications to Building Systems & Algorithmic Challenges, LBNL 2013 [pdf]
  2. Real-Time Optimization for Energy Management in Commercial Buildings, MCS Seminar, 2011 [pdf]
  3. Proactive Energy Management for Next-Generation Building Systems, SimBuild, 2010  [pdf]

Figure 1. Energy Savings with Proactive EM System Deployment at Argonne's APS Office Building

Figure 2. CO2 Profiles at Argonne's APS Office Building