Towards automatic classification of private households using electricity consumption data

Beckel, Christian; Sadamori, Leyna; Santini, Silvia

doi:10.1145/2422531.2422562

Cited by 50 publications

(59 citation statements)

References 17 publications

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“…Modified box-plot rules is a method that decides the value's outlier degree according to the distance between the value and the central value. R is the Coefficient variance ratio of users and industry monthly power consumption, 1 CV is the Coefficient variance t of monthly electricity consuming, 2 CV is the Coefficient variance t of industries' monthly electricity consuming. And what industry the user belongs to will be decided by the monthly power consumption, and the daily peak.…”

Section: Index Calculationmentioning

confidence: 99%

Index System of the Identification of Electricity Stealing and Leakage Research

Tang¹,

Cong²,

Li³

et al. 2017

dtcse

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Abstract. With the development of smart grid, data mining makes identification of users of electricity stealing and leakage a reality. This paper systematically studied the data of electricity information for unusualness detection. Firstly, it analyzed the behavior features of those users suspicious of electricity stealing and leakage, constructed a preliminary behavior identification index system. Then it used the discrimination analysis and correlation analysis method for quantitative judgment on the feasibility of screening evaluation index, excluding some indexes, finally got three first level indexes and eight secondary indexes. The paper presented three new indexes, including coefficient of dispersion of A&C phase current's (voltage's) difference and the clustering number of monthly electricity load pattern. Calculation method of the index was also described in detail. The index system has shown its feasibility by application of Clustering method and the fuzzy neural network.

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Section: Index Calculationmentioning

confidence: 99%

Index System of the Identification of Electricity Stealing and Leakage Research

Tang¹,

Cong²,

Li³

et al. 2017

dtcse

View full text Add to dashboard Cite

show abstract

“…Unsupervised learning based on SOMs have been used in [25][26][27] to classify, filter and identify customers' consumption patterns in order to learn both their distribution and topology, and segment the demand patterns for electrical customers. Additionally frequencybased indices and hourly LP curve methods were proposed in [28].…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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“…We refer to these characteristics as the properties of a household. In a previous study, we have defined a set of properties that can be considered both useful to know as well as likely to be inferable with reasonable accuracy from electricity consumption data [12]. In this paper, we build upon our previous work and present the design, implementation, and evaluation of CLASS -a system that estimates the value of the properties of a household for which electricity consumption data is available.…”

Section: Introductionmentioning

confidence: 99%

“…These, in turn, need to increasingly offer premium services in order to attract new customers -as well as to retain existing ones. Through in-depth interviews with employees of four different Swiss energy providers, we have identified energy consulting as a representative example of such services [12]. The goal of energy consulting is to provide practical recommendations to customers in order to allow them to reduce their overall energy consumption, thus saving money.…”

Section: Introductionmentioning

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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Towards automatic classification of private households using electricity consumption data

Cited by 50 publications

References 17 publications

Index System of the Identification of Electricity Stealing and Leakage Research

Index System of the Identification of Electricity Stealing and Leakage Research

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Automatic socio-economic classification of households using electricity consumption data

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