Statistical modelling of district-level residential electricity use in NSW, Australia

Boulaire, Fanny; Higgins, Andrew; Foliente, Greg; McNamara, Cheryl

doi:10.1007/s11625-013-0206-8

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…; Boulaire et al. ), we identified a number of socioeconomic variables to predict energy consumption. The candidate variables were sought from Office of National Statistics sources and had to be available at all of the geographies studied.…”

Section: Methods and Datamentioning

confidence: 99%

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Horta¹,

Keirstead²

2016

J of Industrial Ecology

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SummaryUrban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.

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Section: Methods and Datamentioning

confidence: 99%

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Horta¹,

Keirstead²

2016

J of Industrial Ecology

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“…In Australia, energy bills and Australian Bureau of Statistics (ABS) household expenditure data have been used to model household energy use in Sydney [41], and to estimate New South Wales aggregated electricity consumption at the census collection district level [42]. Due to the sporadic deployment of residential smart meters across Australia, as well as privacy concerns (see Section 0), more detailed analysis of residential electricity load profiles in Australia has been limited by the small number of published datasets containing residential customer load data at high temporal resolution.…”

Section: Energy Use and Household Characteristicsmentioning

confidence: 99%

Cluster-based characterisation of Australian apartment electricity demand and its implications for low-carbon cities

Roberts¹,

Haghdadi²,

Bruce³

et al. 2019

Preprint

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Understanding of residential electricity demand has application in efficient building design, network planning and broader policy and regulation, as well as in planning the deployment of energy efficiency technologies and distributed energy resources with potential emissions reduction benefits as well societal and household cost savings. Very few studies have explored the specific demand characteristics of apartments, which house a growing proportion of the global urban population. We present a study of apartment electricity loads, using a dataset containing a year of half-hourly electricity data for 6000 Australian households, to examine the relationship between dwelling type, demographic characteristics and load profile. The focus on apartments, combined with the size of the data set, and the representative seasonal load profiles obtained through clustering full annual profiles, is unique in the literature. We find that median per-occupant household electricity use is 21% lower for apartments than for houses and that, on average, apartments have lower load factor and higher daily load variability, and show greater diversity in their daily peak times, resulting in a lower coincidence factor for aggregations of apartment loads. Using cluster analysis and classification, we also show the impact of dwelling type on the shape of household electricity load profiles.

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“…Current approaches to predicting urban building energy consumption at different spatial levels are functions of the characteristics of buildings [17][18][19], urban information [5,6,20], or rely on sensor-based data-driven approaches [14]. As the effects of rapid increases in complexity and human activities in cities are amplified [21], understanding and managing the end use patterns of energy demand in spatiotemporal dimensions [5], taking into account the uncertainties of human behavior becomes more imperative.…”

Section: Related Workmentioning

confidence: 99%

“…The essential challenge facing academic researchers is thus to find a way to anticipate future changes in human activities (human mobility) that will enable them to make reliable predictions of future urban energy demand. A growing body of research has explored the predictability of human mobility [31][32][33][34][35], and new models are continually being proposed that provide more reliable predictions of energy consumption [6,18,23,36,37]. Studies that have focused primarily on predicting human mobility [33,34,38,39] and building human mobility-based predictive models [13] have taken a number of different approaches.…”

Section: Related Workmentioning

confidence: 99%

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Taylor

2017

Applied Energy

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Urbanization is causing a significant increase in the amount, diversity, and complexity of human activities, all of which have a substantial impact on energy consumption. Current approaches to predicting energy demand at different spatiotemporal levels are functions of the characteristics of either individual buildings or cities and their occupancy levels, or are data-driven, typically taking the form of sensor-based modeling. Nevertheless, accounting for both the spatial and temporal effects of heterogeneous human behavior patterns on buildings' energy use at the city level remains a challenge. In this paper, we examine the temporal manifestation of the fluctuations of energy use in urban buildings driven by spatial mobility patterns of the population. We then present an urban-level spatiotemporal approach for predicting buildings' energy demand. Using a full year of individual positional records from an online social networking platform (Twitter), we introduce a multivariate autoregressive model in reduced principle component analysis space to create monthly predictions of residential building electricity demand generated across 801 spatial divisions that account for 68% of the electricity used by buildings in the City of Chicago, through a spatial autoregressive model. This model represents an important step forward, incorporating the spatiotemporal energy use fluctuations of urban population activities to create more reliable predictions of demand in future cities.

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Statistical modelling of district-level residential electricity use in NSW, Australia

Cited by 21 publications

References 12 publications

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Cluster-based characterisation of Australian apartment electricity demand and its implications for low-carbon cities

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

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