Sensitivity and uncertainty analysis of England's housing energy model

Hughes, Martin; Palmer, Jason; Cheng, Vicky; Shipworth, David

doi:10.1080/09613218.2013.769146

Cited by 45 publications

(41 citation statements)

References 8 publications

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“…The heating demand temperature (which usually reflects the thermostat set-point temperature used in a dwelling to control the heating system) and the duration of heating period (i.e. the number of hours that the heating system is on) have the greatest influence on a home's space heating energy use and associated CO 2 emissions, as revealed by sensitivity analysis conducted on BREDEM (Building Research Establishment Domestic Energy Model) class models Hughes, Palmer, Cheng, & Shipworth, 2013). Several papers have examined internal temperatures (see below), but in particular the variability between homes and the time course of temperatures over a day has received little attention.…”

Section: Introductionmentioning

confidence: 99%

The shape of warmth: temperature profiles in living rooms

Huebner

McMichael

Shipworth

et al. 2014

Building Research & Information

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The most commonly used family of models in representing the UK building stock, BREDEM (the Building Research Establishment Domestic Energy Models), assume that all homes exhibit the same heating pattern and hence can be expected to have similar temperature profiles over the course of a day. The presented research shows that homes differ significantly in their respective temperature profile over the course of the day. A cluster analysis performed on temperature data from 275 living rooms in English homes over three winter months resulted in four different clusters of temperature profiles. The clusters differ significantly in their shape, as revealed by visual inspection, and supported by significant differences in minimum and maximum temperatures and temperature variability across the day. About 40% of homes showed a bimodal temperature pattern as assumed under BREDEM. However, the remaining 60% showed very different profiles. These findings challenge the assumption that one standard pattern fits all homes. Different temperature demand profiles have important implications for future peak power demands, particularly if domestic space heating is switched to electricity. It is also helpful for relating occupant demographics to appropriate forms of fabric retrofit.

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Section: Introductionmentioning

confidence: 99%

The shape of warmth: temperature profiles in living rooms

Huebner

McMichael

Shipworth

et al. 2014

Building Research & Information

Self Cite

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“…Figure 3 is an extension of a previous work published on the uncertainties of the CHM [19] (see [19] for the uncertainties in this source model). This paper uses the CHM uncertainty model as a starting point for an emerging spatial, area-based urban, domestic energy model of uncertainties.…”

Section: The Ncrf Outcome Parametric Uncertainty Using a Concept Mapmentioning

confidence: 96%

“…This paper uses the CHM uncertainty model as a starting point for an emerging spatial, area-based urban, domestic energy model of uncertainties. Figure 3 shows the CM section derived from [19] plus the additional CM sections describing uncertainty sources from NCRF: 'Full SAP input' and 'refinement/validation'. uncertainty model as a starting point for an emerging spatial, area-based urban, domestic energy model of uncertainties.…”

Section: The Ncrf Outcome Parametric Uncertainty Using a Concept Mapmentioning

confidence: 99%

“…uncertainty model as a starting point for an emerging spatial, area-based urban, domestic energy model of uncertainties. Figure 3 shows the CM section derived from [19] plus the additional CM sections describing uncertainty sources from NCRF: 'Full SAP input' and 'refinement/validation'. In Figure 3, the green colour corresponds to the sources of uncertainties, and orange represents the issues (activities and disagreements) causing the uncertainties in the corresponding sources.…”

Section: The Ncrf Outcome Parametric Uncertainty Using a Concept Mapmentioning

confidence: 99%

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Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

2017

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Domestic energy modelling is complex, in terms of user input and the approach used to define the model; therefore, there is an increase in the sources of uncertainties. Previous efforts to perform sensitivity and uncertainty analyses have focused on national energy models, while in this research, the objective is to extend traditional sensitivity analysis and use a local framework combining principal component regression and Monte Carlo Simulation. Therefore, in our method the total amount of the energy output's variance is decomposed, in relative terms, according to the contribution of the different predictor parameters. Our framework provides compelling evidence that local area characteristics are important in energy modelling and those national and regional indexes and values may not properly reflect the local conditions, resulting in programmes and interventions that will be sub-optimal. Furthermore, our uncertainty methodology uses a three dimensional integrative taxonomy and a concept map. The concept map identified concrete terminal causes of uncertainty within the taxonomic framework of sources, issues, sub-issues and a further abstraction of those quantities in terms of accuracy and precision. Understanding uncertainties in this way provides a possible framework for modellers, policy makers and data collectors to improve practice in key areas and to reduce uncertainty.

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“…The predicted temperatures and SAP were used to identify the potential overheating risk. Nevertheless care had to be taken with using SAP given its limitations in relation to accuracy (Kelly et al, 2012, Hughes et al, 2013. Solar exposure and Direct Insolation data for Case B were calculated using Solar Analysis module in Bentley's Microstation V8i (Select Series 3) (Bentley, 2012).…”

Section: Introductionmentioning

confidence: 99%