Characterisation of Australian apartment electricity demand and its implications for low-carbon cities. (1st August 2019)

• Record Type: Journal Article
• Authors: Roberts, Mike B.; Haghdadi, Navid; Bruce, Anna; MacGill, Iain
• Is Part Of: Energy. Volume 180(2019)
• Page Start: 242
• Page End: 257
• Publication Date: 2019-08-01
• Subjects: Apartments -- Residential electricity demand -- Load profiles -- Cluster analysis -- Load aggregation -- Low-carbon cities; ABS Australian Bureau of Statistics -- ADMD After Diversity Maximum Demand -- BOM Bureau of Meteorology -- CER Commission for Energy Regulation -- CF Coincidence Factor -- CV Coefficient of Variation -- DER Distributed Energy Resource -- h/h household -- HVAC Heating, ventilation, and air conditioning -- LF Load Factor -- MLR Multinomial Logistic Regression -- NMI National Meter Identifier -- NSW New South Wales -- PCA Principal Component Analysis -- PC Profile Class -- PV Photovoltaic -- RFE Recursive Feature Elimination -- SCM Self-Consumption Metric -- SGSC Smart Grid Smart City; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.222
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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 and 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 6, 000 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. Graphical abstract: Image 109771 Highlights: Median per-occupant energy use 21% lower for apartments than for houses. Apartment loads have higher variability, lower load factor and coincidence factor. After diversity aggregation benefits significant for up to fifty residential loads. Dwelling type is a significant factor in clustering of household load profiles. The high diversity of household load profiles precludes simple categorisation.