Titled: "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine", the paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district.

The study 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 supports planning and decision making at strategic, tactical and operational levels. This kind of 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 and 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.

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https://www.mdpi.com/1996-1073/12/22/4287