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NEBULA: A National Scale Dataset for Neighbourhood-Level Urban Building Energy Modelling for England and Wales

Grace Colverd, Ronita Bardhan, Jonathan Cullen

TL;DR

NEBULA addresses the scarcity of geo-located, building-level energy data for UK neighbourhood-scale energy modelling by providing a privacy-preserving, open-source dataset for England and Wales. It combines seven themes—building stock, typology, age, region, urbanisation, climate, and socio-demographics—into 242 variables indexed at the postcode level, integrating DESNZ energy data, Verisk building stock, HADUK climate data, and census information to enable bottom-up modelling with $EUI = \frac{E}{\sum A_H}$. The dataset undergoes rigorous cleaning, validation, and thresholding to yield 609,964 domestic postcodes, with both filtered and unfiltered versions available. Technical validation uses a Morris sensitivity analysis to show that postcode total floor area is a strong driver of model outputs, while height introduces more nonlinearity; results support NEBULA’s utility for energy benchmarks, planning, and predictive modelling at the neighbourhood scale. Overall, NEBULA provides a scalable, regionally consistent, openly accessible resource to support national net-zero planning and UBEM analyses in the UK.

Abstract

Buildings are significant contributors to global greenhouse gas emissions, accounting for 26% of global energy sector emissions in 2022. Meeting net zero goals requires a rapid reduction in building emissions, both directly from the buildings and indirectly from the production of electricity and heat used in buildings. National energy planning for net zero demands both detailed and comprehensive building energy consumption data. However, geo-located building-level energy data is rarely available in Europe, with analysis typically relying on anonymised, simulated or low-resolution data. To address this problem, we introduce a dataset of Neighbourhood Energy, Buildings, and Urban Landscapes (NEBULA) for modelling domestic energy consumption for small neighbourhoods (5-150 households). NEBULA integrates data on building characteristics, climate, urbanisation, environment, and socio-demographics and contains 609,964 samples across England and Wales.

NEBULA: A National Scale Dataset for Neighbourhood-Level Urban Building Energy Modelling for England and Wales

TL;DR

NEBULA addresses the scarcity of geo-located, building-level energy data for UK neighbourhood-scale energy modelling by providing a privacy-preserving, open-source dataset for England and Wales. It combines seven themes—building stock, typology, age, region, urbanisation, climate, and socio-demographics—into 242 variables indexed at the postcode level, integrating DESNZ energy data, Verisk building stock, HADUK climate data, and census information to enable bottom-up modelling with . The dataset undergoes rigorous cleaning, validation, and thresholding to yield 609,964 domestic postcodes, with both filtered and unfiltered versions available. Technical validation uses a Morris sensitivity analysis to show that postcode total floor area is a strong driver of model outputs, while height introduces more nonlinearity; results support NEBULA’s utility for energy benchmarks, planning, and predictive modelling at the neighbourhood scale. Overall, NEBULA provides a scalable, regionally consistent, openly accessible resource to support national net-zero planning and UBEM analyses in the UK.

Abstract

Buildings are significant contributors to global greenhouse gas emissions, accounting for 26% of global energy sector emissions in 2022. Meeting net zero goals requires a rapid reduction in building emissions, both directly from the buildings and indirectly from the production of electricity and heat used in buildings. National energy planning for net zero demands both detailed and comprehensive building energy consumption data. However, geo-located building-level energy data is rarely available in Europe, with analysis typically relying on anonymised, simulated or low-resolution data. To address this problem, we introduce a dataset of Neighbourhood Energy, Buildings, and Urban Landscapes (NEBULA) for modelling domestic energy consumption for small neighbourhoods (5-150 households). NEBULA integrates data on building characteristics, climate, urbanisation, environment, and socio-demographics and contains 609,964 samples across England and Wales.
Paper Structure (21 sections, 14 equations, 4 figures, 4 tables)

This paper contains 21 sections, 14 equations, 4 figures, 4 tables.

Figures (4)

  • Figure 1: Example of the dual method for matching buildings to postcode Visualisation code adapted from Lipson lipson_plotting_2021. Satellite image downloaded from openstreetmap_contributors__planet_2017. Footprints via Digital Map Data © Verisk (The GeoInformation Group Ltd.) the_geoinformation_group_limited_digital_nodate
  • Figure 2: Stages of processing with the count of postcodes.
  • Figure 3: Sensitivity analysis results using Morris parameter screening for generating Total building floor area per postcode.
  • Figure 4: Regional performance for Morris parameter analysis