A study for clustering method to generate Typical Load Profiles for Smart Grid

Kim, Young Il; Kang, Shin-Jae; Ko, Jong-Min; Choi, Seung-Hwan

doi:10.1109/icpe.2011.5944675

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…This characteristic is related to the magnitude of the weekly harmonic component, , and the magnitude of the daily harmonic component, . The following attribute is proposed to model this characteristic: (4) For example, the large commercial load profiles normally have a consistent daily pattern and not much load drop between the weekday and weekend load, which leads value to be close to 1. For commercial load profiles that have a higher weekday load level than its weekend load level (high and low ), their value will be larger than 1.…”

Section: A Classification Treementioning

confidence: 99%

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Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain

Zhong

Tam

2015

IEEE Trans. Power Syst.

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Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling.

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Section: A Classification Treementioning

confidence: 99%

“…4 CAFDs are used in level 2 of the classification tree to define the categories' parameter spaces. The first CAFD, divides the CAT I.1 load profiles into the following two groups: There are percentages associated with each load class in Table III.…”

Section: B Comparison With Current Practicesmentioning

confidence: 99%

Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain

Zhong

Tam

2015

IEEE Trans. Power Syst.

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“…The analysis of those data can support the definition of customer's classes. In [7] DM techniques are used to establish load profiles taking into account the effect of weather conditions.…”

Section: A Load Profiling Reviewmentioning

confidence: 99%

“…Indeed, MASGriP -a MultiAgents Smart Grid Simulation Platform is proposed in [7], and it models micro and smart grids players, such as domestic customers, small to large industries and commerce, besides aggregator entities such as Curtailment Service Providers (CSP) and Virtual Power Players (VPP). Each player is represented by one agent with the capability of representing the actual corresponding player and simulating its actions.…”

Section: Introductionmentioning

confidence: 99%

Load profiling tool to support smart grid operation scenarios

Ramos

Praça

Vale

et al. 2014

2014 IEEE PES T&D Conference and Exposition

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This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers' consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

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“…This could in turn allow the retail price to better reflect the instantaneous cost of generating and transmitting electricity, which varies significantly with the total demand for electricity. The possible uses of Smart Meters have received growing attention in recent academic literature [1,2,3,4,5,6,7], as well as lay literature.…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition and segmentation of smart meter data

McDonald

Pudney

Rong

2014

ANZIAMJ

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In Australia, Smart Meters automatically provide electricity suppliers with half-hour energy use data for each customer. This data can be used to classify customers into different categories. To this end, electricity supplier agl provided misg participants with data from 772 anonymous Victorian customers, collected between 2011-07-16 and 2012-01-30, and the corresponding series of half-hour temperature readings for Melbourne. The goals were to identify a small number of load profiles that could be used to classify customers, and to identify which customers have significant cooling loads and which customers have significant heating loads. For each customer there was a time series of 9552 half-hour periods, which made the dimensionality of the problem too high for cluster analysis of the entire sample data. Therefore analysis proceeded in two phases. First, the data were explored using various

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A study for clustering method to generate Typical Load Profiles for Smart Grid

Cited by 22 publications

References 8 publications

Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain

Hierarchical Classification of Load Profiles Based on Their Characteristic Attributes in Frequency Domain

Load profiling tool to support smart grid operation scenarios

Pattern recognition and segmentation of smart meter data

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