Next Article in Journal
Forecasting Oil Price Using Web-based Sentiment Analysis
Next Article in Special Issue
A Machine Learning Pipeline for Demand Response Capacity Scheduling
Previous Article in Journal
Optimization, Transesterification and Analytical Study of Rhus typhina Non-Edible Seed Oil as Biodiesel Production
Previous Article in Special Issue
Comprehensive Quality Assessment Algorithm for Smart Meters
Open AccessArticle

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

Department of Management Information Systems, Faculty of Engineering and Information Technology, An-Najah National University, Nablus, Palestine
Current address: An-Najah National University, P.O.Box: 7, Nablus, Palestine.
Energies 2019, 12(22), 4287; https://doi.org/10.3390/en12224287
Received: 25 September 2019 / Revised: 29 October 2019 / Accepted: 7 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Data Analytics in Energy Systems)
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. View Full-Text
Keywords: data mining; data visualization; K-means clustering; support vector machine classifier; principal components analysis; elbow method; silhouette analysis data mining; data visualization; K-means clustering; support vector machine classifier; principal components analysis; elbow method; silhouette analysis
Show Figures

Graphical abstract

MDPI and ACS Style

AbuBaker, M. Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine. Energies 2019, 12, 4287.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop