An optimal control strategy with enhanced robustness for air-conditioning systems considering model and measurement uncertainties

Na Zhu, Kui Shan, Shengwei Wang, Yongjun Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


Model-based optimal controls in HVAC systems involve uncertainties due to model uncertainties and measurement uncertainties. These uncertainties affect the accuracy and reliability of the outputs of optimal control strategies, and therefore affect the energy and environmental performance of buildings. This study proposes a method to enhance the robustness of optimal control strategies. A fuzzy approach is adopted to predict the errors in models outputs. Such predicted errors are then used to correct the model outputs. The method is validated in an optimal control strategy for HVAC cooling water systems. The operation data of a real building system is used to validate the error prediction method. A simulation platform is built to validate the enhanced strategy. Measurement uncertainties are deliberately added to the simulated system for validation tests. Test results indicate that the method is effective in predicting the errors in model outputs. Significant energy savings are achieved compared with the conventional optimal control method.
Original languageEnglish
Pages (from-to)540-550
Number of pages11
JournalEnergy and Buildings
Publication statusPublished - 1 Oct 2013


  • Air-conditioning system
  • Fuzzy c-means clustering
  • Machine learning
  • Measurement uncertainty
  • Model uncertainty
  • Optimal control strategy

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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