Develop Load Shape Dictionary Through Efficient Clustering Based on Elastic Dissimilarity Measure

Load shape dictionary (LSD) is a useful tool for utilizing the enormous amount of smart meter data to understand customers' electricity consumption behaviors. To tackle the big data challenge as well as to better capture the load shape features, this article develops a bilevel LSD generation fr...

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Bibliographic Details
Published inIEEE transactions on smart grid Vol. 12; no. 1; pp. 442 - 452
Main Authors Liang, Huishi, Ma, Jin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Clustering
Computational efficiency
Computing costs
Customers
derivative dynamic time warping (DDTW)
Dictionaries
Economic forecasting
elastic dissimilarity measure
Electrical loads
Electricity consumption
Electricity meters
Energy management
Feature extraction
load profile
Shape
Shape measurement
smart meter data
Smart meters
Time measurement
Time series analysis
Warping
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Summary:Load shape dictionary (LSD) is a useful tool for utilizing the enormous amount of smart meter data to understand customers' electricity consumption behaviors. To tackle the big data challenge as well as to better capture the load shape features, this article develops a bilevel LSD generation framework to cluster and index the residential load profiles into a neat local LSD and a global LSD based on the Derivative Dynamic Time Warping (DDTW) elastic dissimilarity measure. Different from the classic Dynamic Time Warping (DTW), DDTW works on the derivative of the raw data to avoid DTW's problem of pathological alignments. To reduce the computational cost, a fast DDTW (FDDTW) is proposed to speed up the DDTW calculation. Based on the generated bilevel LSD, analytic approaches are proposed to extract features from the data indexed by the LSD to reveal useful information of customers' electricity consumption behaviors. Numerical experiments on real premise data verify the effectiveness of the proposed methodology in terms of clustering performance and computational efficiency. Our analysis suggests that the proposed methodology can be applied to improve load forecasting, tariff design and demand response (DR) customer targeting.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.3017777