Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study

By Fintan McLoughlin, A. Duffy, Michael F. Conlon. 2012

437 citations recorded by the source corpus.

Abstract This paper examines the influence of dwelling and occupant characteristics on domestic electricity consumption patterns by analysing data obtained from a smart metering survey of a representative cross section of approximately 4200 domestic Irish dwellings. A multiple linear regression model was applied to four parameters: total electricity consumption, maximum demand, load factor and time of use (ToU) of maximum electricity demand for a number of different dwelling and occupant socio-economic variables. In particular, dwelling type, number of bedrooms, head of household (HoH) age, household composition, social class, water heating and cooking type all had a significant influence over total domestic electricity consumption. Maximum electricity demand was significantly influenced by household composition as well as water heating and cooking type. A strong relationship also existed between maximum demand and most household appliances but, in particular, tumble dryers, dishwashers and electric cookers had the greatest influence over this parameter. Time of use (ToU) for maximum electricity demand was found to be strongly influenced by occupant characteristics, HoH age and household composition. Younger head of households were more inclined to use electricity later in the evening than older occupants. The appliance that showed the greatest potential for shifting demand away from peak time use was the dishwasher.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/4ded3379ab5602edb1ab4526d47263191bbf7542

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Citations in the corpus

Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics (2015, positive): “The review also identified that dwellings with an electric space heating system [11–14,23,24,34,36], airconditioning [13,18,29,41,43,46] or electric water heating system [9,11,13,23,29,34,43] had greater electricity demand.”
Revealing Household Characteristics from Smart Meter Data (2014, background): “also investigated the correlation between electricity consumption data and household characteristics [25].”
Evaluation of time series techniques to characterise domestic electricity demand (2013, methodology): “In order to characterise electricity demand in a presentable format, four electrical parameters were used from a previous paper by the same authors [2].”
Electricity load forecasting: a systematic review (2020, background): “[55] examined residential electricity consumption patterns in Irish based on occupant socio-economic and dwelling variables.”
Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data (2016, background): “Although there exist correlations between some characteristics and attributes, such as maximum demand, they are weak and do not describe intraday behaviors [17], [18, p.”
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, background): “Thus, we seek to cluster load customers in a meaningful way by taking into consideration these inherent daily and intradaily variations [24] that can/will improve current practices on load forecasting.”
The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings (2015, background): “Common data collection methods used in dwelling level studies are personal interviews [8,20], phone surveys [21], electricity meter readings provided by energy providers [14,22,23], household electricity monitoring [5,12,21,24], including submetering of appliances [20,25], questionnaires [7,12,20,23”