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Multi-Domain Feature Extraction for Improved Clustering of Smart Meter Data

Multi-Domain Feature Extraction for Improved Clustering of Smart Meter Data The advent of smart grid is a revolution that has enabled power distribution in a more efficient way. However, load forecasting, demand response management and accurate consumer load profiling using smart meter data continue to be challenging industry and research problems. Clustering is an efficient technique for load profiling. K-means clustering algorithm for clustering electricity consumers based on raw meter data directly result in cumbersome, redundant and inefficient computations. This paper presents a methodology for reducing the raw data set dimension via features extraction and cluster the load profiles based on computed features. The feature set formed comprises of Singular Values by Singular Value Decomposition and Wavelet Energy Entropy of approximate and detailed Coefficients. K means Clustering technique is used. The proposed method enables efficient and quick clustering and at the same time the information content in load profiling is preserved. The time consumed for clustering of feature set formed is found to be much less than that of raw data set. By comparing the Silhouette Values K = 6 was found to be the optimal number of clusters with average silhouette coefficient around 0.79. Clustering of load profiles both for Raw Data Set as well as computed Feature Set are compared by evaluating average silhouette value, number of negative silhouettes and computation time for clustering and Silhouette Coefficient was found to be 0.79 by proposed methodology showing better clustering result as compared to raw dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

Multi-Domain Feature Extraction for Improved Clustering of Smart Meter Data

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Publisher
Springer Journals
Copyright
Copyright © Springer Nature Singapore Pte Ltd. 2020
eISSN
2199-4706
DOI
10.1007/s40866-020-00080-w
Publisher site
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Abstract

The advent of smart grid is a revolution that has enabled power distribution in a more efficient way. However, load forecasting, demand response management and accurate consumer load profiling using smart meter data continue to be challenging industry and research problems. Clustering is an efficient technique for load profiling. K-means clustering algorithm for clustering electricity consumers based on raw meter data directly result in cumbersome, redundant and inefficient computations. This paper presents a methodology for reducing the raw data set dimension via features extraction and cluster the load profiles based on computed features. The feature set formed comprises of Singular Values by Singular Value Decomposition and Wavelet Energy Entropy of approximate and detailed Coefficients. K means Clustering technique is used. The proposed method enables efficient and quick clustering and at the same time the information content in load profiling is preserved. The time consumed for clustering of feature set formed is found to be much less than that of raw data set. By comparing the Silhouette Values K = 6 was found to be the optimal number of clusters with average silhouette coefficient around 0.79. Clustering of load profiles both for Raw Data Set as well as computed Feature Set are compared by evaluating average silhouette value, number of negative silhouettes and computation time for clustering and Silhouette Coefficient was found to be 0.79 by proposed methodology showing better clustering result as compared to raw dataset.

Journal

Technology and Economics of Smart Grids and Sustainable EnergySpringer Journals

Published: Apr 4, 2020

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