Quantifying the Measurement Error on England and Wales EPC Ratings

Crawley, Jenny; Biddulph, P.; Northrop, Paul J.; Wingfield, J; Oreszczyn, Tadj; Elwell, Clifford A.

doi:10.3390/en12183523

Cited by 36 publications

(23 citation statements)

References 12 publications

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“…While the data-driven EQMs produce similar results throughout all building age classes, the engineering EQM increases in prediction accuracy towards newer buildings until 1990. This is in line with findings in literature of higher measurement errors for buildings with lower energetic efficiency in England and Wales (Crawley et al 2019), thus older buildings with less strict regulations, which can be explained by the underlying data quality. As mentioned, engineering EQMs require exact inputs and expert knowledge to produce viable outcomes.…”

Section: Benchmarking Against the Engineering Energy Quantification Msupporting

confidence: 92%

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Wenninger

Wiethe

2021

Bus Inf Syst Eng

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To achieve ambitious climate goals, it is necessary to increase the rate of purposeful retrofit measures in the building sector. As a result, Energy Performance Certificates have been designed as important evaluation and rating criterion to increase the retrofit rate in the EU and Germany. Yet, today’s most frequently used and legally required methods to quantify building energy performance show low prediction accuracy, as recent research reveals. To enhance prediction accuracy, the research community introduced data-driven methods which obtained promising results. However, there are no insights in how far Energy Quantification Methods are particularly suited for energy performance prediction. In this research article the data-driven methods Artificial Neural Network, D-vine copula quantile regression, Extreme Gradient Boosting, Random Forest, and Support Vector Regression are compared with and validated by real-world Energy Performance Certificates of German residential buildings issued by qualified auditors using the engineering method required by law. The results, tested for robustness and systematic bias, show that all data-driven methods exceed the engineering method by almost 50% in terms of prediction accuracy. In contrast to existing literature favoring Artificial Neural Networks and Support Vector Regression, all tested methods show similar prediction accuracy with marginal advantages for Extreme Gradient Boosting and Support Vector Regression in terms of prediction accuracy. Given the higher prediction accuracy of data-driven methods, it seems appropriate to revise the current legislation prescribing engineering methods. In addition, data-driven methods could support different organizations, e.g., asset management, in decision-making in order to reduce financial risk and to cut expenses.

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Section: Benchmarking Against the Engineering Energy Quantification Msupporting

confidence: 92%

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Wenninger

Wiethe

2021

Bus Inf Syst Eng

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“…The climate variables are derived data from re-analysis of multiple original datasets and therefore do not suffer from missing data, although other issues may exist that we have not investigated. EPC data quality has been investigated previously and found to suffer from inaccuracies [24,25]. Researchers are encouraged to treat EPC data with caution and consider potential data quality issues, in addition to the potential bias that requiring an analytic sample to have 100% EPC data will entail (Section 5.4).…”

Section: Data Quality Analysismentioning

confidence: 99%

The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain

et al. 2021

Self Cite

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The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes were collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data were collected, the variables available and the representativeness of the sample compared to national estimates. It is intended to be a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

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“…; hence, the need to link the data to a model of buildings and their characteristics. of EPCs and some of their underlying data by the Ministry for Housing, Communities & Local Government (MHCLG) (2017a, 2017b), research has identified considerable uncertainty around the accuracy of ratings and how representative they are of actual energy use (Crawley et al 2019;Jenkins et al 2017;Summerfield et al 2019). These works are important because EPCs are seen by government as a key descriptor of the whole building stock and are used as a means of predicting changes in energy use resulting from the upgrading of building energy performance.…”

Section: Energy Data and Energy Performance Certificates (Epcs)mentioning

confidence: 99%

Energy use intensities in London houses

Liddiard

Godoy-Shimizu

Ruyssevelt

et al. 2021

Buildings and Cities

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This research compares domestic metered energy data, for both gas and electricity consumption, against characteristics drawn from a building stock model of Greater London, UK. The energy analyses are limited to houses (single-building, single household) with one standard electricity meter and one mains gas meter as the principal subset. This provides a sample of almost 1.2 million, or 75%, of London's stock of houses. Energy use was normalised by calculated floor area, providing an energy use intensity (EUI; kWh/ m 2 /yr), which allows properties of all sizes to be compared. Examination of EUIs of each built form versus Energy Performance Certificate (EPC) current energy efficiency (Asset Rating value) indicates weak, or very weak, correlation between the two, particularly for electricity. PRACTICE RELEVANCEThe study demonstrates how a detailed building stock model may be used for the analysis of metered energy use in the buildings of urban areas: in this case houses. The analyses examine some aspects of the data that constitute the stock model, such as built form and age, which are held at the scale of what can be considered to be individual buildings. The model currently covers Greater London, but is being built for other urban areas of England and Wales, thus giving it the potential to aid different layers of government or other actors in their efforts to reduce energy use in the building stock-both domestic and non-domestic. Some aspects of the model, such as the calculation of floor areas, should be replicable, where these data are accessible.

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Quantifying the Measurement Error on England and Wales EPC Ratings

Cited by 36 publications

References 12 publications

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

The SERL Observatory Dataset: Longitudinal Smart Meter Electricity and Gas Data, Survey, EPC and Climate Data for over 13,000 Households in Great Britain

Energy use intensities in London houses

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