Enabling urban-scale energy modelling: a new spatial approach

Abstract

Urban-scale energy modelling provides an ideal tool for studying nondomestic energy consumption and emissions reduction at the community level. In principle, an approach based on the characteristics of individual commercial premises and buildings is attractive but poses a number of challenges, the most immediate of which is deciding precisely what to model. For a range of reasons connected with their self-contained nature, individual non-domestic buildings would ideally be selected. However, the main information sources available, digital mapping and business taxation data, are not based on "buildings" and do not use the concept, thus making an automatic approach problematic. At the same time, manual identification of the distinct buildings in a city is not a practical proposition because of the numbers involved.The digital mapping and business taxation data are brought together in the local land and property gazetteer (LLPG). An analysis of the relationships between the relevant elements, namely building polygons and premises attracting business taxation, allowed a unit to be defined that matches the definition of a "building" in most circumstances and can be applied without the need for human intervention. This novel approach provides a firmer basis for modelling non-domestic building energy at the urban scale.(198 words) 3

Modelling a whole building stock: domestic, non-domestic and mixed use

“…But there is no mention of blocks of flats or mixing with non-domestic premises, even though the latter also form part of the model. The more automated approach developed by Taylor, Fan, and Rylatt (2014) excludes domestic premises and thus does not address their mixing with the non-domestic stock, even though such premises may occur within the building(s) represented. Although these could contain multiple premises and therefore potentially multiple activities, the extent of mixing was not addressed explicitly, though non-domestic energy use was modelled for each building, but again excluding any domestic premises present.…”

“…Once this process of geolocation, stratification and polygon capture is complete, a boundary can be set around the premises or collection of premises, which does not split either a premises or an OSTopo polygon. This is called a self-contained unit (SCU) (Evans et al, 2016;Taylor et al, 2014). In many cases, a SCU is just a representation of a single building, but in some cases to avoid splitting premises it can include two or more adjoining buildings; or in the case of some schools, large factories and other campus-based sites, it may include multiple non-adjacent buildings and is referred to as a 'poly-SCU' (Evans et al, 2016, p. 10).…”

Modelling household spatial energy intensity consumption patterns for building envelopes, heating systems and temperature controls in cities

“…This review will provide a useful understanding of current efforts made by energy modellers to provide a solution for the urban energy consumption patterns planning. Examples include: [5], who addressed the scale of the modelling by proposing a 'whole building' self-contained unit (SCU) as a physically meaningful unit that has its own energy metering and its relationship to buildings. This approach allows city-scale modelling based on the characteristics of an individual building in Le-icester.…”

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

“…All of these techniques require extensive data inputs, on the built environment, measured energy consumption, activity patterns, or other sources. A notable exception is Taylor and colleagues (), who use statistical downscaling to take official government statistics on energy consumption and combine it with census data to increase the spatial resolution to a 1‐square‐kilometer grid. The results were used to examine future bioenergy scenarios for the UK, which rely on high‐spatial‐resolution demand data.…”

“…Our goal in this article is to expand upon this latter approach. Taylor and colleagues () do not specifically consider alternative methods for statistical downscaling in their article. But an evaluation of these methods is important in order to understand their data requirements, accuracy, and hence suitability to different applications.…”