Methods for generating TLPs (typical load profiles) for smart grid-based energy programs

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

doi:10.1109/ciasg.2011.5953331

Cited by 51 publications

(34 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Kim et al [10] performed a similar analysis by clustering electricity usage from high voltage consumers in South Korea to determine typical load profiles using 15-min data. They had 3183 high voltage customers from the Korea Electric Power Corporation (KEP-CO) whose meter data were used for this study.…”

Section: Introductionmentioning

confidence: 99%

Clustering analysis of residential electricity demand profiles

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Clustering analysis of residential electricity demand profiles

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The processing of these data, for a specific period of study, can lead to the formulation of representative load curves for each type of consumer [2]. The representative load curves or load profiles can be defined as the demand pattern of a single or a group of consumers over a period of time.…”

Section: Introductionmentioning

confidence: 99%

Electricity customer characterization based on different representative load curves

Panapakidis

Alexiadis

Papagiannis

2012

2012 9th International Conference on the European Energy Market

View full text Add to dashboard Cite

Load profiling provides the necessary information about daily demand patterns for the short and medium-term actions Ƞf retailers and utilities. Consumer characterization is a two stage approach: In the first stage, the daily load curves of each consumer are classified in a certain number of clusters. Each cluster constitutes a load profile. In the second stage, one of these profiles is chosen as representative for the consumer and a new classification takes places between the load profiles of each customer, leading to the formulation of customer classes. This paper examines various approaches for the formulation of the consumer classes during the first stage. A specific profile is chosen and the second stage procedure takes place. A criterion based on cost of purchased electricity is introduced in order to evaluate the results of the clustering of the second stage.Index Terms-Electricity customer characterization; demand side management; load profiles; pattern recognition; I.P. Panapakidis is with the . His special interests are power systems modelling, computation of electromagnetic transients, distributed generation, powerline communications, smart grids and energy policy.

show abstract

“…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

View full text Add to dashboard Cite

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

show abstract

Methods for generating TLPs (typical load profiles) for smart grid-based energy programs

Cited by 51 publications

References 4 publications

Clustering analysis of residential electricity demand profiles

Clustering analysis of residential electricity demand profiles

Electricity customer characterization based on different representative load curves

Pattern recognition and segmentation of smart meter data

Contact Info

Product

Resources

About