The Optimization of Sensitivity Coefficients for the Virtual in Situ Sensor Calibration in a LiBr–H2O Absorption Refrigeration System

  • Peng Wang
  • Kaihong Han
  • Liangdong Ma
  • Sungmin YoonEmail author
  • Yuebin Yu
Conference paper
Part of the Environmental Science and Engineering book series (ESE)


The correct data or information from the building sensing networks plays a vital role in the operation algorithms. The sensor errors usually show a negative effect on the performance of control, diagnosis, and optimization of building energy systems. Thus, the physical working sensors periodically need to be removed to be calibrated by the reference sensors, which will disrupt the normal operation of building systems from time to time. The virtual in situ sensor calibration (VIC), based on the Bayesian inference and Markov chain Monte Carlo methods (MCMC), is an effective approach to handle the systematic and random errors of various working sensors simultaneously. This technology uses the distance function and system models to estimate the true measurements and addresses most of the practical problems in a traditional calibration process. However, the sensitivity coefficient in the definition of distance function is one of the determining factors in the calibration accuracy and how to define it still remains uncertain. Therefore, this study employed the genetic algorithm (GA) to optimize this parameter in a LiBr–H2O absorption refrigeration system. The results revealed that the systematic and random errors of temperature and mass flow rate were reduced considerably with the help of optimized sensitivity coefficients and most of the measurements approached to their true values after the calibration.


Sensor network Virtual in situ calibration Sensitivity coefficient optimization Bayesian MCMC Genetic algorithm 



This work was supported by the National Natural Science Foundation of China (Grant No. 51806029), National Key R&D Program of China (Grant No. 2017YFC0704200), China Postdoctoral Science Foundation Funded Project (Grant No. 2016M590221), and Fundamental Research Funds for the Central Universities (Grant No. DUT18RC(4)054).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Peng Wang
    • 1
    • 2
  • Kaihong Han
    • 1
  • Liangdong Ma
    • 1
  • Sungmin Yoon
    • 3
    • 4
    Email author
  • Yuebin Yu
    • 2
  1. 1.School of Civil EngineeringDalian University of TechnologyDalian CityChina
  2. 2.Durham School of Architectural Engineering and ConstructionUniversity of Nebraska-LincolnOmahaUSA
  3. 3.Division of Architecture and Urban DesignIncheon National UniversityIncheonRepublic of Korea
  4. 4.Institute of Urban Science, Incheon National UniversityIncheonRepublic of Korea

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