Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

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NILMTK: an open source toolkit for non-intrusive load monitoring (2014, background): “In International Conference of Machine Learning and Applications, Miami, FL, USA, 2013.”
Revealing Household Characteristics from Smart Meter Data (2014, background): “In recent studies, researchers increasingly investigated the combination of electricity consumption with side-information [11, 21, 22, 23, 24, 25, 26, 27].”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Stepwise selection was applied to investigate the factors that have a great influence on residential electricity consumption in [141].”
Determinants of high electrical energy demand in UK homes: Socio-economic and dwelling characteristics (2015, unknown): “This finding is consistent with a number of previous studies [11,16,29].”
The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings (2015, methodology): “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”