Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

Räsänen, Teemu; Voukantsis, Dimitris; Niska, Harri; Karatzas, Kostas; Kolehmainen, Mikko

doi:10.1016/j.apenergy.2010.05.015

Cited by 186 publications

(122 citation statements)

References 19 publications

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“…Many of the papers apply K-Means for baseline clustering and compare more advanced methods to this baseline [14][15][16], with inconclusive outcomes regarding the best method for clustering. Some papers make an effort to preprocess the smart meter data; popular preprocessing methods are principal component analysis and factor analysis for dimensionality reduction [17,18] and self-organizing maps for 2 Dimensional representation of the data [3,10]. All identified methods are not particularly well-suited to time series data, such as smart meter data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Electricity Consumption Clustering Using Smart Meter Data

2018

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Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method.

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Section: Literature Reviewmentioning

confidence: 99%

“…Further algorithms like hierarchical clustering [10,11], and random effect mixture models [12,13] are also popular. Many of the papers apply K-Means for baseline clustering and compare more advanced methods to this baseline [14][15][16], with inconclusive outcomes regarding the best method for clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Clustering Using Smart Meter Data

2018

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“…It results in the evaluation of a dimensionless figure of the private consumer's willingness θ. θ = U res · θ target− θ inter−m θ sel f θ price (15) where U r is the basic user response defined in Section 2.4.1. θ price was defined in Eq. (7).…”

Section: Target-based Feedbacksmentioning

confidence: 99%

“…The latter approach was used to model multiple dwellings where individual appliances were first aggregated to produce individual profiles, followed by an aggregation of the generated profiles to a large sample of electrical load under one node [14]. Appliance usage models rely either on the aggregation of measured data from multiple dwellings [15,16], or on databases compiled for a specific country representing the overall market [17,18,19]. The advantage of using a database is that it bypasses the need for an extensive and exhaustive work of data collection.…”

Section: Introductionmentioning

confidence: 99%

Modelling home electricity management for sustainability: The impact of response levels, technological deployment & occupancy

Louis

Calò

Leiviskä

et al. 2016

Energy and Buildings

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“…Table 1 represents a comprehensive summary for the main studies related to electrical load profiles, clarifying the study area with the applied analytical approaches. Electricity production and consumption were investigated to forecast electricity load consumption in [5] using a genetic algorithm, in [6] using a data-based methodology, and in [7] using support vector machine for regression (SVR) and multilayer perceptron (MLP) for district or single household level. Whereas in [8], a semi-parametric approach based on generalized additive models theory was suggested.…”

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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Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

Cited by 186 publications

References 19 publications

Electricity Consumption Clustering Using Smart Meter Data

Electricity Consumption Clustering Using Smart Meter Data

Modelling home electricity management for sustainability: The impact of response levels, technological deployment & occupancy

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

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