Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

Räsänen, Teemu; Ruuskanen, Juhani; Kolehmainen, Mikko

doi:10.1016/j.apenergy.2007.10.012

Cited by 68 publications

(33 citation statements)

References 10 publications

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“…Models were proposed in [17][18][19] to construct LPs for household electricity in order to determine energy use, forecast usage, predict charge in future energy use, design the power network, and to improve efficiency of energy build structure. to the approaches developed in [21][22] to reduce the total energy consumption in periods of expected peak load.…”

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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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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“…Evaluating the clusters along with the current tariffs of each of the customers, they detect examples of inefficient billing practices (e.g., in case there is a poor correlation between discriminatory factors and actual load patterns) [20]. Many approaches related to this use socalled self-organizing maps (SOMs) to cluster a large number of different households based on their electricity consumption [21,22,23]. For instance, Figueiredo et al use this type of unsupervised learning to identify clusters of households with similar consumption behavior [21].…”

Section: Related Workmentioning

confidence: 99%

“…Their results are based on electricity consumption traces from 165 households in Portugal collected at a 15-minute granularity. Other researchers also include different type of information to the analysis of plain electricity consumption data [22,23]. Sanchez et al [22], for instance, compute specific features out of the data and feed these features to a self-organizing map (SOM) along with additional information obtained through questionnaires.…”

Section: Related Workmentioning

confidence: 99%

“…Sanchez et al [22], for instance, compute specific features out of the data and feed these features to a self-organizing map (SOM) along with additional information obtained through questionnaires. Instead of using electricity consumption to train the SOM, Räsänen et al rely on properties of the dwelling [23]. This way, the authors make households in the same cluster comparable with respect to their dwelling characteristics.…”

Section: Related Workmentioning

confidence: 99%

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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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“…In the past, SOMs have also been applied to the electricity industry. Rasanen et al [3] applied SOMs to create comparison groups so that customers who exhibited similar building characteristics could compare electricity use against each other. Dominguez et al [4] used SOMs to analyse electrical load data from a group of buildings as well as environmental and electricity tariff information in order to achieve economic and energy savings.…”

Section: Introductionmentioning

confidence: 99%

Analysing domestic electricity smart metering data using self organising maps

McLoughlin¹,

Duffy²,

Conlon³

2012

CIRED 2012 Workshop: Integration of Renewables Into the Distribution Grid

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Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

Cited by 68 publications

References 10 publications

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

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

Analysing domestic electricity smart metering data using self organising maps

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