Hybrid least squares support vector machines for short term predictive analysis

Zuriani, Mustaffa and Ernawan, Ferda and M. H., Sulaiman and Syafiq Fauzi, Kamarulzaman (2017) Hybrid least squares support vector machines for short term predictive analysis. In: The 3rd International Conference on Control, Automation and Robotics (ICCAR 2017) , 24-26 April 2017 , Nagoya, Japan. pp. 571-574.. ISBN 978-150906087-0

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Abstract

Moth-flame Optimization (MFO) algorithm is a relatively new optimization algorithm which is classified as Swarm Intelligence (SI). It is inspired by unique behavior of moths in nature. Despite its young age, this algorithm has been proven to be able to address many optimization problems. With respect to that matter, this work introduces a new hybrid approach of MFO with Least Squares Support Vector Machines (termed as MFO-LSSVM). With such hybridization, the LSSVM hyper-parameters are fine-tuned by the MFO. Hence, the generalization in prediction can be improved. Realized in load data, the efficiency of the proposed model is compared against three comparable hybrid algorithms and measured based on three statistical criteria. An experimental study demonstrate that the MFO-LSSVM is able to produce better prediction results compared to the identified hybrid algorithms. Therefore the established hybrid model presents the potential to be applied to short term load prediction.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Least squares support vector machines; Mothflame algorithm; Optimization; Swarm intelligence
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Computer System And Software Engineering Faculty of Electrical & Electronic Engineering
Depositing User:	Mrs. Neng Sury Sulaiman
Date Deposited:	09 Apr 2018 07:22
Last Modified:	11 Apr 2023 07:04
URI:	http://umpir.ump.edu.my/id/eprint/17656
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