The North East Scotland Energy Monitoring Project: Exploring relationships between household occupants and energy usage

In-use monitoring of buildings: An overview and classification of evaluation methods

“…Both have been developed for temperate climates and so their application in tropical locations could be limited. Figure 6 Methods to evaluate energy performance (* [42,47], *2 Not presented in this paper) Table 2 Indoor parameters, PMV and PPD ranges in the standards [46] A c c e p t e d M a n u s c r i p t…”

“…In addition, another line of research focuses on monitoring buildings to explain better the effects of occupancy and building operation [29][30][31][32][33][34], aiming also at changing behavioural patters [35][36][37][38]. Most of these studies use, or intent to make use of large datasets of building monitoring data, usually measured at very small intervals and employing internet/intranet infrastructures and networks [39][40][41][42][43].…”

The Role of Digital Trace Data in Supporting the Collection of Population Statistics – the Case for Smart Metered Electricity Consumption Data

“…Whilst a number of large scale energy monitoring datasets are becoming available for analysis, unless additional information is known about the characteristics of households, it is very difficult to provide further explanations of the patterns observed (see also Craig et al, 2014) (2012) used this dataset to characterise households based on their temporal load profiles, identifying key features such as the timing and relative magnitude of peak demand. They recognised that the magnitude of power import (demand) was strongly influenced by household composition (such as the number of residents and the presence of children), whilst household and dwelling characteristics appeared to influence the timing of use.…”

“…Nevertheless, a range of studies including the Department for Environment, Food and Rural Affairs (DEFRA)/Energy Saving Trust ‘Powering the Nation’ project (Zimmermann et al, ), academic research (Craig et al, ), national statistical office work (Caroll et al, ), and the large‐scale Energy Demand Research Programme (AECOM, ) and Low Carbon Network Fund trials (Haben et al, ) have all illustrated the role of household characteristics as drivers of energy use. In the commercial context, energy management services such as Onzo () claim to have accumulated extensive databases of household energy smart meter data, and their development of commercial services based on the data suggests that household behaviours and routines can be usefully inferred through high temporal resolution energy monitoring data.…”

“…Whilst a number of large scale energy monitoring datasets are becoming available for analysis, unless additional information is known about the characteristics of households, it is very difficult to provide further explanations of the patterns observed (see also Craig et al, ) and thus impossible to develop techniques to infer those household characteristics from such data. As far as we are aware, the only attempt to carry out this kind of analysis to date used data from the Irish Commission for Energy Regulation's Smart Metering Electricity Consumer Behaviour Trials (CER, ).…”