Authors: Janković, Radmila 
Amelio, Alessia
Ranjha, Zulfiqar Ali
Title: Classification of Energy Consumption in the Balkans using Ensemble Learning Methods
Journal: 2nd International Conference on Advancements in Computational Sciences, ICACS 2019
Conference: 2nd International Conference on Advancements in Computational Sciences, ICACS 2019; Lahore; Pakistan; 18 February 2019 through 20 February 2019
Issue Date: 11-Apr-2019
ISBN: 978-969972101-4
DOI: 10.23919/ICACS.2019.8689000
Abstract: 
This paper explores the use of ensemble learning methods for classifying the energy consumption from (i) gross domestic product, (ii) CO2 emissions, and (iii) total number of population. The proposed analysis extends the previous research where prediction of the energy consumption values was performed using regression strategies. The experiment involves energy use data from five different Balkan countries, which is collected in the period 1995-2014. The energy consumption classes are obtained from the energy use values by K-Medians and analysed. Then, multiclass ensembles of support vector machine and linear discriminant analysis are used for classification of the energy consumption given the gross domestic product, the CO2 emissions, and the total number of population. The classification task is compared with a traditional multiclass support vector machine approach. The obtained results are very promising.
Keywords: classification | clustering | energy consumption | ensemble learning | pattern recognition
Publisher: IEEE
Project: Development of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education 

Show full item record

SCOPUSTM   
Citations

1
checked on Sep 16, 2022

Page view(s)

27
checked on Sep 16, 2022

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.