Record number :
2449466
Title of article :
Clustering Electricity Big Data for Consumption Modeling Using Comparative Strainer Method for High Accuracy Attainment and Dimensionality Reduction
Author/Authors :
Azizi ، Elnaz - Tarbiat Modares University , Kharrati Shishavan ، Hamed - University of Tabriz , Mohammadi-ivatloo ، Behnam - University of Tabriz , Mohammadpour Shotorbani ، Amin University of British Columbia
Pages :
7
From page :
1
To page :
7
Abstract :
In smart grid, the relation between grid and customer is bidirectional. Therefore, analyzing load consumption patterns is essential for optimal and efficient operation and planning of smart grid in addition to precise load forecasting. However, emergence of the advanced metering infrastructure, which enables a two-way flow of data and power consumption between consumers and suppliers, has resulted in data explosion in smart grid applications. Because of the volume and velocity of data generation in recent years, traditional data analysis methods are inefficient. Therefore, new methods of analyzing such as “data mining”, which segments data before analyzing and manipulating, are recommended. Clustering, as a well-known method in data mining, has extensively been employed in recent electricity industry. This article argues that even though clustering methods can be directly applied to raw data of electricity consumption, this approach is inefficient since it requires storage and processing of high-dimensional and high-volume data. Hence, it would be more beneficial to cluster consumption data in a space of reduced dimension. In this paper, the authors propose a new structure for dimension reduction to refine the electricity consumption data. This method aims to increase the accuracy and decrease the time of clustering. The results are compared with the famous method of dimension reduction, component analysis (PCA). The authors evaluate the proposed technique using datasets from Kaveh, an industrial area in Iran.
Keywords :
Smart grid , Advanced metering infrastructure , Data mining , Clustering , principal component analysis
Journal title :
Journal of Energy Management and Technology
Serial Year :
2019
Link To Document :
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