Electricity customer characterization based on different representative load curves

Panapakidis, Ioannis P.; Alexiadis, Minas C.; Papagiannis, Grigoris K.

doi:10.1109/eem.2012.6254761

Cited by 20 publications

(19 citation statements)

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“…The clustering algorithms are applied separately on the load data. By applying the knee-point detection method on the WCBCR curve [10], the optimal number of clusters is 5. For each consumer in the clusters, the mean weekly profile is extracted.…”

Section: Extraction Of the Load Profilesmentioning

confidence: 99%

Implementation of price-based demand response programs through a load pattern clustering process

2014

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This study addresses the problem of the optimal design of price-based Demand Response (DR) programs such as RealTime Pricing (RTP) utilizing the load profiling tool. The proposed model corresponds to a profit maximization problem of a retailer that serves a group of residential consumers. Through a clustering process the consumers are grouped together in several clusters. For each cluster a dynamic tariff is offered that is specially design to fit to the typical load pattern of the cluster. The sensitivity of the demand over the offered selling price is modeled through a price responsive demand function. Apart from implementing different demand functions, the flexibility of the proposed model offers a selection of different clustering algorithms and different retailer pricing policy.

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Section: Extraction Of the Load Profilesmentioning

confidence: 99%

Implementation of price-based demand response programs through a load pattern clustering process

2014

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“…1) The Mean Square Error or Error Function J, which refers to the distance of each vector from its cluster centroid: (13) 2) The cm which is the ratio of the mean infra-set distance between the input vectors in the same cluster and the infra-set distance between the clusters' centroids: (14) 3) The MIA which is the average of the distances between each input vector assigned to the cluster and its centroid: (15) 4) The WCBCR which refers to the ratio of the distance of each vector from its cluster centroid and the geometric mean of the inner-distances of the set:…”

Section: B Adequacy Measuresmentioning

confidence: 99%

Improvements in the clustering validity indexes of the load profiling methodology

Panapakidis

Dagoumas²,

Alexiadis

et al. 2013

2013 10th International Conference on the European Energy Market (EEM)

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In the recent years the utilization of the load profiling tool for tracking the demand patterns is gathering momentum. There is variety of different clustering algorithms for the formation of daily load curve clusters. Their effectiveness is tested by a set of validity indexes or adequacy measures. This paper examines the behavior of all the adequacy measures that have been proposed in the related literature. We propose an alternative form of the measures that involves a weighting factor that refers to the variance of each element of the vector that represents the demand pattern. This fact increases the accuracy of the dissimilarity measures within and among the clusters. The data sample refers to the daily load curves of an existing high voltage industry within the Greek region and the period of study is the years 2003-2011. This vast amount of data is sufficient for assessing the load profiling methodology.

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“…Zhou et al [13] and Panapakidis et al [14] used smaller datasets for their clustering analyses of electricity loads. Zhou et al used 72 load profiles from six different consumers in China and Panapakidis et al used a mix of 150 residential, commercial, and industrial consumers in Greece.…”

Section: Introductionmentioning

confidence: 99%

Clustering analysis of residential electricity demand profiles

et al. 2014

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h i g h l i g h t sOptimal k-means clustering finds seasonal groups of residential electricity use. We find that each season has two nominal groups. One group typically uses more expensive electricity than the other. Regression analysis allows for insight as to which homes will be in which cluster. a b s t r a c tLittle is known about variations in electricity use at finely-resolved timescales, or the drivers for those variations. Using measured electricity use data from 103 homes in Austin, TX, this analysis sought to (1) determine the shape of seasonally-resolved residential demand profiles, (2) determine the optimal number of normalized representative residential electricity use profiles within each season, and (3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the kmeans clustering algorithm. Then probit regression was performed to determine if homeowner survey responses could serve as predictors for the clustering results. This analysis found that Austin homes fall into one of two seasonal groups with some homes using more expensive electricity (from a wholesale electricity market perspective) than others. Regression results indicate that variables such as if someone works from home, hours of television watched per week, and education levels have significant correlations with average profile shape, but might vary across seasons. The results herein also indicate that policies such as time-of-use or real-time electricity structures might be more likely to affect lower income households during some high electricity use parts of the year.

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Electricity customer characterization based on different representative load curves

Cited by 20 publications

References 10 publications

Implementation of price-based demand response programs through a load pattern clustering process

Implementation of price-based demand response programs through a load pattern clustering process

Improvements in the clustering validity indexes of the load profiling methodology

Clustering analysis of residential electricity demand profiles

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