Next Article in Journal
Reliability Field Test of the Air–Surface Temperature Ratio Method for In Situ Measurement of U-Values
Next Article in Special Issue
Robust Building Energy Load Forecasting Using Physically-Based Kernel Models
Previous Article in Journal
Optimal Load-Tracking Operation of Grid-Connected Solid Oxide Fuel Cells through Set Point Scheduling and Combined L1-MPC Control
Previous Article in Special Issue
Bayesian Energy Measurement and Verification Analysis
Correction published on 6 September 2018, see Energies 2018, 11(9), 2353.
Article

A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

Physical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UK
*
Author to whom correspondence should be addressed.
Energies 2018, 11(4), 802; https://doi.org/10.3390/en11040802
Received: 1 February 2018 / Revised: 20 February 2018 / Accepted: 22 March 2018 / Published: 30 March 2018
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series data that is often recorded. This paper presents a dynamic Bayesian-based method to estimate the thermophysical properties of building elements from in-situ measurements. The analysis includes Markov chain Monte Carlo (MCMC) estimation, priors, uncertainty analysis, and model comparison to select the most appropriate model. Data from two case study dwellings is used to illustrate model performance; U-value estimates from the dynamic and static methods are within error estimates, with the dynamic model generally requiring much shorter time series than the static model. The dynamic model produced robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low, when external temperatures had large daily variation, and measurements were subjected to direct solar radiation. Further, the probability distributions of parameters may provide insights into the thermal performance of elements. Dynamic methods such as that presented herein may enable wider characterisation of the performance of building elements as built, supporting work to reduce the performance gap. View Full-Text
Keywords: heat transfer; Bayesian statistics; in-situ measurements; inverse modelling; uncertainty analysis; U-value; dynamic modelling heat transfer; Bayesian statistics; in-situ measurements; inverse modelling; uncertainty analysis; U-value; dynamic modelling
Show Figures

Figure 1

MDPI and ACS Style

Gori, V.; Biddulph, P.; Elwell, C.A. A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error. Energies 2018, 11, 802. https://doi.org/10.3390/en11040802

AMA Style

Gori V, Biddulph P, Elwell CA. A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error. Energies. 2018; 11(4):802. https://doi.org/10.3390/en11040802

Chicago/Turabian Style

Gori, Virginia, Phillip Biddulph, and Clifford A. Elwell 2018. "A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error" Energies 11, no. 4: 802. https://doi.org/10.3390/en11040802

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop