Synthetically derived profiles for representing occupant-driven electric loads in Canadian housing

Armstrong, M. M.; Swinton, M. C.; Ribberink, Hajo; Beausoleil-Morrison, Ian; Millette, Jocelyn

doi:10.1080/19401490802706653

Cited by 114 publications

(79 citation statements)

References 7 publications

(3 reference statements)

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“…In the past, detailed models for residential loads have been presented in the power engineering, environmental studies, and civil engineering literature [11,3,4,9]. However, these models suffer from three problems.…”

Section: Background and Related Workmentioning

confidence: 99%

Markovian models for home electricity consumption

Ardakanian

Keshav

Rosenberg

2011

Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking

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Modelling home energy consumption is necessary for studying demand-response, transformer sizing, and distribution network simulation. Using an existing classification, we propose parsimonious Markovian reference models of home load for each class. We derive models for on-peak periods, offpeak periods, and mid-peak periods. These models are derived using traces based on fine-grained measurements of electricity consumption in 20 homes over four months. We validate the representativeness of our models in a specific application.

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Section: Background and Related Workmentioning

confidence: 99%

Markovian models for home electricity consumption

Ardakanian

Keshav

Rosenberg

2011

Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking

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“…As such, the model allows cross-sectional and longitudinal analyses of behavioural aspects. The latter approach is adopted in all previously reference models, while load-based comprehensive models are scarce (Mansouri et al , 1996;Paatero & Lund, 2006;Armstrong et al , 2009). …”

Section: Prior Considerationsmentioning

confidence: 99%

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

Bætens

Sælens

2015

Journal of Building Performance Simulation

103

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Occupant behaviour has since long been of main interest in the domain of building energy savings and indoor air quality; and its importance is recognized by its wide coverage in literature. In the recent developments of detailed transient building energy simulations, including the occupant behaviour as boundary condition for the thermal comfort and system efficiency calculations has been a major research topic given its significant impact. Simultaneous growing interest in district energy simulations raises similar questions at the aggregate level, where upscaling from the building to an aggregate neighborhood level at the spatial scale of a low-voltage feeder results in a natural regression to the mean lowering uncertainty compared to the level of the household.The presented work starts with the description of StROBe, a stochastic residential occupant behaviour for district energy simulations; integrating the modelling of receptacle loads, internal heat gains, thermostat settings and hot water tappings based on occupancy and activity prerequisites. Given this model, the uncertainty for district energy simulations is addressed. The epistemic uncertainties are aleborated first comparing model results with reference values and denoting local disaggregation of demographic statistics as possible main hiatus of general modelling methods for building energy occupant behaviour used at the neighborhood level. To conclude, the aleatory uncertainty caused by stochastic residential occupant behaviour in integrated district energy simulations are quantified. Here, the expected value of the objective functions have to a large extend the same minimizers as the measures of the proposed robustness. As such, optimizing an objective value for its expected value generally seems to result in a optimum near the optimum of robustness. However, 95 percent of the observed objectives lay between 0.81 and 1.6 times the expected value for a feeder larger than 10 houses and between 0.88 and 1.3 times the expected value for more than 20 houses denoting an overall 'rather small' uncertainty on the possible objective functions caused by user behaviour. Furthermore, we show that the design of the building energy system has its impact on the robustness of the objective criteria and it could thus be minimized as part of an optimiation exercise.

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“…Consumption parameters of electric appliances have been updated based on the studies of Armstrong et al (2009) andStamminger (2008). Further electric appliances monitored in the scope of the Household Electricity Usage Study (Element Energy 2013) have been added to the electricity model.…”

Section: A Crest Modelmentioning

confidence: 99%

Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake

et al. 2016

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Adequately accounting for interactions between Low Carbon Technologies (LCTs) at the building level and the overarching energy system means capturing the granularity associated with decentralised heat and power supply in residential buildings. The approach presented here adds novelty in terms of a realistic socioeconomic differentiation by employing dwelling/household archetypes (DHAs) and neighbourhood clusters at the Output Area (OA) level. These archetypes are combined with a mixed integer linear program (MILP), which is used to generate optimum (minimum cost) technology configurations and operation schedules. Even in the baseline case, i.e. without any LCT penetration, a substantial deviation from the standard load profile (SLP) is encountered, suggesting that for some neighbourhoods this profile is not appropriate. With the application of LCTs this effect is much stronger, including more negative residual load, more variability, and higher ramps with increased LCT penetration, and crucially different between neighbourhood clusters. The main policy implication of the study is the importance of understanding electrical load profiles at the neighbourhood level, because of the consequences they have for investment in the overarching energy system (e.g. transmission and distribution infrastructure, centralised generation plant etc.). Further work should focus on attaining a superior socioeconomic differentiation between households.

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Synthetically derived profiles for representing occupant-driven electric loads in Canadian housing

Cited by 114 publications

References 7 publications

Markovian models for home electricity consumption

Markovian models for home electricity consumption

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake

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