Statistical analysis of drivers of residential peak electricity demand

•Study quantifies how factors drive residential summer peak electricity demand.•Based on detailed demographic and housing survey of over 3000 households.•Model shows good fit with aggregated household consumption on multiple peaks.•Findings highlight key contribution of high temperatures and ducted...

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Bibliographic Details
Published in:Energy and buildings 2017-04, Vol.141, p.205-217
Main Authors: Fan, H., MacGill, I.F., Sproul, A.B.
Format: Article
Language:English
Subjects:
Air conditioners
Air conditioning
Demand
Demand analysis
Demographics
Demography
Electric power demand
Electric utilities
Electricity
Electricity consumption
Electricity demand
Energy consumption
Energy demand
Energy efficiency
Energy management
Factors
Households
Housing
Influence
Mathematical models
Modelling
Ownership
Peak demand
Peak load
Power efficiency
Residential
Residential energy
Smart grid
Smart grid technology
Statistical analysis
Swimming
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Summary:•Study quantifies how factors drive residential summer peak electricity demand.•Based on detailed demographic and housing survey of over 3000 households.•Model shows good fit with aggregated household consumption on multiple peaks.•Findings highlight key contribution of high temperatures and ducted AC to peak.•Findings: play a key role in advising decision and policy making for peak shaving. Growth in peak electricity demand poses considerable challenges for utilities seeking to ensure secure, reliable yet affordable energy provision. A better understanding of the key drivers of residential peak electricity demand could assist in better managing peak demand growth through options including demand-side participation and energy efficiency programs. However, such analysis has often been constrained by the limited data available from standard household metering, as well as typically low direct engagement by utilities with households regarding their energy use. This paper presents a study analysing and modeling residential peak demand in the greater Sydney region using data from Australia’s largest Smart Grid study to date. The dataset includes household level half hour consumption matched to surveyed information including housing type, demographics and appliance ownership. A range of statistical and modeling techniques are applied to determine key drivers for household demand at times of network peaks. The analysis and model quantify how different factors drive residential peak demand on hot summer days. Key drivers identified include air-conditioning ownership, the number of household occupants, swimming pool ownership, and clothes dryer usage. Finally, the model is used to investigate the potential aggregate network peak implications of changes in household demographics and appliance ownership.
ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2017.02.030