A Hybrid Physics-Based and Data Driven Approach to Optimal Control of Building Cooling/Heating Systems

S. A. Vaghefi, M. A. Jafari, J. Zhu, J. Brouwer, Y. Lu

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

This work integrates a physics-based model with a data driven time-series model to forecast and optimally manage building energy. Physical characterization of the building is partially captured by a collection of zonal energy balance equations with parameters estimated using a least squares estimation (LSE) technique and data initially generated from the EnergyPlus building model. A generalized Cochran-Orcutt estimation technique is adopted to describe the data generated from these simulations. The combined forecast model is then used in a model predictive control (MPC) framework to manage heating and cooling set points. This work is motivated by the practical limitations of simulation-based optimizations. Once the forecast model is established capturing sufficient statistical variability and physical behavior of the building, there will be no more need to run EnergyPlus in the optimization routine.

Original languageEnglish (US)
Article number6913017
Pages (from-to)600-610
Number of pages11
JournalIEEE Transactions on Automation Science and Engineering
Volume13
Issue number2
DOIs
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Building energy management
  • model predictive control (MPC)
  • multiobjective mathematical programming (MMP)
  • thermal comfort

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