Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine

Maher AbuBaker
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Maher AbuBaker: Department of Management Information Systems, Faculty of Engineering and Information Technology, An-Najah National University, Nablus, Palestine

Energies, 2019, vol. 12, issue 22, 1-29

Abstract: This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

Keywords: data mining; data visualization; K-means clustering; support vector machine classifier; principal components analysis; elbow method; silhouette analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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