Estimation of Middle-East oil consumption using hybrid meta-heuristic algorithms

Haruna, Chiroma and Khan, Abdullah and Abubakar, Adamu and Saadi, Younes and Abdullahi Muaz, Sanah and Ya’u Gital, Abdulsalam and Shuib, Liyana (2019) Estimation of Middle-East oil consumption using hybrid meta-heuristic algorithms. In: Lecture Notes in Electrical Engineering. Springer Nature Singapore, Singapore, pp. 139-150. ISBN 978-981-13-1797-2

	PDF (Scopus) Restricted to Registered users only Download (113kB) | Request a copy
	PDF - Published Version Restricted to Repository staff only Download (576kB) | Request a copy

Abstract

The consumption of energy has significantly increased in theworld during the preceding decade. Two-third of energy requirements are produced by oil and gas. Estimation of oil consumption can give clues on the future energy consumption. In this study, the effectiveness of three hybrid metaheuristic algorithms, namely, Cuckoo Search Neural Network (CSNN), Artificial Bee Colony Neural Network (ABCNN), and Genetic Algorithm Neural Network (GANN) were investigated for the estimation of oil consumption. The simulation results showed that the CSNN improved the estimation accuracy of oil consumption over ABCNN and GANN whereas GANN improved convergence speed over CSNN and ABCNN. The study has shown that in terms of accuracy, the CSNN is appropriate for the estimation of oil consumption. In terms of convergence speed, GANN is the most suitable algorithms

Item Type:	Book Chapter
Additional Information:	7132/74274
Subjects:	Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	31 Jan 2020 11:36
Last Modified:	31 Jan 2020 11:36
URI:	http://irep.iium.edu.my/id/eprint/74274

Actions (login required)

View Item