Using pattern recognition to identify habitual behavior in residential electricity consumption

Abreu, Joana; Pereira, Francisco C.; Ferrão, Paulo

doi:10.1016/j.enbuild.2012.02.044

Cited by 111 publications

(59 citation statements)

References 5 publications

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“…Through the analysis of coarsegrained consumption data (e.g. in the order of 1 measurement per 15 or 30 minutes), for instance, it has been shown that energy providers can identify usage patterns in the electricity consumption data to predict future electricity consumption [6] or model daily routines to improve a providers's supply management [1]. Other researchers have proposed approaches that can cluster hundreds of households into groups of consumers according to their load profile [20,24] or estimate socio-economic characteristics of a household [3].…”

Section: Analysis Of Electricity Consumption Datamentioning

confidence: 99%

Occupancy Detection from Electricity Consumption Data

Kleiminger

Beckel

Staake

et al. 2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

193

110

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Detecting when a household is occupied by its residents is fundamental to enable a number of home automation applications. Current systems for occupancy detection usually require the installation of dedicated sensors, like passive infrared sensors, magnetic reed switches or cameras. In this paper, we investigate the suitability of digital electricity meters -which are already available in millions of households worldwide -to be used as occupancy sensors. To this end, we have collected fine-grained electricity consumption data along with ground-truth occupancy information for 5 households during a period of about 8 months. Our results show that using common classification methods it is possible to achieve occupancy detection accuracies of more than 80%.

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Section: Analysis Of Electricity Consumption Datamentioning

confidence: 99%

Occupancy Detection from Electricity Consumption Data

Kleiminger

Beckel

Staake

et al. 2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

193

110

View full text Add to dashboard Cite

show abstract

“…Various analysis methodologies have been developed: they have used the regression model [37][38][39][40]; time-series analysis [41][42][43][44][45][46][47][48][49]; and clustering techniques [46][47][48][49][50][51][52][53][54]. However, most analyses have been aimed at short-and medium-term demand forecasting; relatively few analyses have been directed at tailor-made feedback.…”

Section: Analysis Methodology Of Residential Electricity-use Datamentioning

confidence: 99%

“…Beckel et al [53] adopted supervised machine learning techniques to estimate specific characteristics of a household, e.g., its socioeconomic status, dwelling, or appliance stock, from its electricity-use data collected from 4232 households in Ireland. Abreu et al [48] focused mainly on the variation in load profiles in the course of a year. The authors gathered load profiles of up to 14 months for 15 households in Portugal and performed a cluster analysis of those profiles to identify each household's routines over a year.…”

Section: Analysis Methodology Of Residential Electricity-use Datamentioning

confidence: 99%

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

2017

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Abstract:Residential smart metering and energy feedback have attracted worldwide attention toward reducing energy consumption and building a sustainable society. Many theoretical studies have suggested the importance of personalized information; however, few feedback demonstrations have focused on household lifestyle. This paper presents a pilot program of energy feedback reports based on analytical methods to show the relationship between electricity consumption and household lifestyle in Japan. One type of report was for households with a night-oriented lifestyle, which were classified by means of frequency analysis; it was evident that such households should shift to a healthy, environmentally friendly, morning-oriented lifestyle. Another type of report was based on cluster analysis: it pinpointed the dates and times when the household consumed much more electricity than with its regular routine. Through panel data regression analysis, it was found that the reports contributed to reducing daily household electricity consumption-as long as a boomerang effect could be avoided. It was also found that the feedback effect was enhanced by activation of consciousness, norms, and motives. It was observed that activation required a good understanding of the characteristics of electricity consumption and lifestyles of each household.

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“…Focusing on data measured with a granularity of 15, 30, or 60 minutes, other related approaches detect consumption patterns of households over a long time frame [11,17,18,19]. To energy providers, applications based on such data are of particular interest as this is the type of data that was col-lected during most of the smart meter trials so far.…”

Section: Related Workmentioning

confidence: 99%

Automatic socio-economic classification of households using electricity consumption data

Beckel

Sadamori

Santini

2013

Proceedings of the Fourth International Conference on Future Energy Systems

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Interest in analyzing electricity consumption data of private households has grown steadily in the last years. Several authors have for instance focused on identifying groups of households with similar consumption patterns or on providing feedback to consumers in order to motivate them to reduce their energy consumption. In this paper, we propose to use electricity consumption data to classify households according to pre-defined "properties" of interest. Examples of these properties include the floor area of a household or the number of its occupants. Energy providers can leverage knowledge of such household properties to shape premium services (e.g., energy consulting) for their customers. We present a classification system -called CLASS -that takes as input electricity consumption data of a private household and provides as output the estimated values of its properties. We describe the design and implementation of CLASS and evaluate its performance. To this end, we rely on electricity consumption traces from 3,488 private households, collected at a 30-minute granularity and for a period of more than 1.5 years. Our evaluation shows that CLASS -relying on electricity consumption data only -can estimate the majority of the considered household properties with more than 70% accuracy. For some of the properties, CLASS's accuracy exceeds 80%. Furthermore, we show that for selected properties the use of a priori information can increase classification accuracy by up to 11%.

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Using pattern recognition to identify habitual behavior in residential electricity consumption

Cited by 111 publications

References 5 publications

Occupancy Detection from Electricity Consumption Data

Occupancy Detection from Electricity Consumption Data

Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan

Automatic socio-economic classification of households using electricity consumption data

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