Modelling household electricity load profiles based on Danish time-use survey data

Foteinaki, Kyriaki; Li, Rongling; Rode, Carsten; Andersen, Rune Korsholm

doi:10.1016/j.enbuild.2019.109355

Cited by 41 publications

(18 citation statements)

References 28 publications

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“…Indeed, state-of-the-art models for residential demand forecasting are based on either survey methods [14,22,37] or high time-resolution data [38] (e.g., provided by SMs). For instance, Ge et al [20] modelled daily load profiles through a generative network, setting a training dataset of real load profiles and mapping such profiles' probability distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, the smart meter as a monitoring system allows for the integration with the transport sector, representing an additional challenge for electrification at the household level, as discussed by Liu et al [43]. However, energy system models often assume a standard load profile of the households, as in Foteinaki et al [14], not entirely reflecting ongoing changes. As discussed by Hayn et al [12] a more detailed segmentation of the household sector, through sociodemographic factors, is essential to assess the future development of residential load profiles, thus investigating its impacts on a wider scale.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It should be remarked that the identification of energy poverty key parameters is of extreme importance to policymakers when planning investments aimed at reducing the residential sector energy consumption. However, in energy systems modelling, households are often represented by a standard load profile, as in Foteinaki et al [14], based on historical data, not entirely reflecting ongoing changes. A more detailed segmentation of the residential sector, such as Yilmaz et al [15], allows us to achieve representative electricity demand profiles and to improve existing models concerning using standard patterns that fit all households.…”

Section: Introductionmentioning

confidence: 99%

“…The defined clusters are thereby representative of the Italian households consumptions and behaviors, with accurate approximations; thus, by conducting measurements within the representative clusters, the results can be generalized and exploited to characterize the possible energy savings of the different types of households at a national level (e.g., waste reduction, appliances replacement) in the perspectives of awareness raising, training, and information campaigns. Moreover, the results regarding the load profiles of both total and single-appliance energy consumptions can be applied to validate load profile simulation models [14,15,[20][21][22] or to decrease the time steps of more general energy consumption estimation models, in particular in exploiting a bottom-up approach such as MOIRAE [23].…”

Section: Introductionmentioning

confidence: 99%

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Italian Household Load Profiles: A Monitoring Campaign

2020

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The increasing share of renewable energy sources on the supply side, as well as the so-called electrification pathways on the demand side, has led to peculiar challenges for electrical systems: Indeed, the increasing load demand has to be balanced from the supply-side viewpoint. In particular, the residential sector contributes to nearly 26% of the final energy consumption in Europe, suggesting that a further understanding of households’ consumptions and load profiles is needed to support an energy transition. In this context, this paper contributes to the existing discussion by proposing a sociodemographic analysis of Italian households’ load profiles using a smart metering experimental study, while also considering the households in energy poverty conditions. For the sake of generality, results are presented based on a previously proposed household segmentation of the Italian residential sector. The outcomes point out three prominent peaks on load profiles for all the identified clusters, with a notable distinction in intensity. Where children are present, a higher load profile is noted, reaching a maximum value of 600 W of absorbed power between 19:30 and 22:30. Conversely, households in an energy poverty condition show a relatively regular load profile, ranging from a minimum of 110 W of absorbed power in baseload conditions at night time to a maximum of 280 W in the evening hours. The findings in this paper are in agreement with existing research in the field, and accordingly the study proposes a better focus on domestic appliances and sociodemographic parameters.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Italian Household Load Profiles: A Monitoring Campaign

2020

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“…More specifically, the choice of a temporal data granularity (data sampling frequency) for specifying consumption load profile features has a crucial impact on the results of any action or assessment, as discussed in the literature [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], see Table 1 . This table summarizes for each potential action or assessment the time resolution (data granularity) and time horizon (time slice) envisaged for the works related to load profiles in households.…”

Section: Introductionmentioning

confidence: 99%

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Hernández

Sánchez-Sutil

Caño

et al. 2020

Sensors

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Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.

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