4.7 Article

Estimation of thermophysical properties from in-situ measurements in all seasons: Quantifying and reducing errors using dynamic grey-box methods

Journal

ENERGY AND BUILDINGS
Volume 167, Issue -, Pages 290-300

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2018.02.048

Keywords

U-value; Heat transfer; In-situ measurements; Bayesian statistics; Error quantification; Grey-box methods; Uncertainty analysis; Inverse modelling

Funding

  1. EPSRC Centre for Doctoral Training in Energy Demand (LoLo) [EP/L01517X/1, EP/H009612/1]
  2. RCUK Centre for Energy Epidemiology (CEE) [EP/K011839/1]
  3. EPSRC [EP/K011839/1] Funding Source: UKRI
  4. Engineering and Physical Sciences Research Council [EP/K011839/1] Funding Source: researchfish

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Robust characterisation of the thermal performance of buildings from in-situ measurements requires error analysis to evaluate the certainty of estimates. A method for the quantification of systematic errors on the thermophysical properties of buildings obtained using dynamic grey-box methods is presented, and compared to error estimates from the average method. Different error propagation methods (accounting for equipment uncertainties) were introduced to reflect the different mathematical description of heat transfer in the static and dynamic approaches. Thermophysical properties and their associated errors were investigated using two case studies monitored long term. The analysis showed that the dynamic method (and in particular a three thermal resistance and two thermal mass model) reduced the systematic error compared to the static method, even for periods of low internal-to-external average temperature difference. It was also shown that the use of a uniform error as suggested in the ISO 9869-1:2014 Standard would generally be misrepresentative. The study highlighted that dynamic methods for the analysis of in-situ measurements may provide robust characterisation of the thermophysical behaviour of buildings and extend their application beyond the winter season in temperate climates (e.g., for quality assurance and informed decision making purposes) in support of closing the performance gap. (C) 2018 The Authors. Published by Elsevier B.V.

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