A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error
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 serie...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/4/802 |
| _version_ | 1811306687656624128 |
|---|---|
| author | Virginia Gori Phillip Biddulph Clifford A. Elwell |
| author_facet | Virginia Gori Phillip Biddulph Clifford A. Elwell |
| author_sort | Virginia Gori |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:49:57Z |
| format | Article |
| id | doaj.art-8188d139772746e2809f94a4a5eb2966 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:49:57Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8188d139772746e2809f94a4a5eb29662022-12-22T02:53:30ZengMDPI AGEnergies1996-10732018-03-0111480210.3390/en11040802en11040802A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced ErrorVirginia Gori0Phillip Biddulph1Clifford A. Elwell2Physical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UKPhysical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UKPhysical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UKThe 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.http://www.mdpi.com/1996-1073/11/4/802heat transferBayesian statisticsin-situ measurementsinverse modellinguncertainty analysisU-valuedynamic modelling |
| spellingShingle | Virginia Gori Phillip Biddulph Clifford A. Elwell A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error Energies heat transfer Bayesian statistics in-situ measurements inverse modelling uncertainty analysis U-value dynamic modelling |
| title | A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error |
| title_full | A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error |
| title_fullStr | A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error |
| title_full_unstemmed | A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error |
| title_short | A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error |
| title_sort | bayesian dynamic method to estimate the thermophysical properties of building elements in all seasons orientations and with reduced error |
| topic | heat transfer Bayesian statistics in-situ measurements inverse modelling uncertainty analysis U-value dynamic modelling |
| url | http://www.mdpi.com/1996-1073/11/4/802 |
| work_keys_str_mv | AT virginiagori abayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror AT phillipbiddulph abayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror AT cliffordaelwell abayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror AT virginiagori bayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror AT phillipbiddulph bayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror AT cliffordaelwell bayesiandynamicmethodtoestimatethethermophysicalpropertiesofbuildingelementsinallseasonsorientationsandwithreducederror |