Hybrid least squares support vector machines for short term predictive analysis

Hybrid least squares support vector machines for short term predictive analysis

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 respec...

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Main Authors: Zuriani, Mustaffa, Ernawan, Ferda, M. H., Sulaiman, Syafiq Fauzi, Kamarulzaman
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Subjects:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/17656/
http://umpir.ump.edu.my/id/eprint/17656/
http://umpir.ump.edu.my/id/eprint/17656/1/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/17656/7/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis%201.pdf
id ump-17656
recordtype eprints
spelling ump-176562018-04-09T07:22:00Z http://umpir.ump.edu.my/id/eprint/17656/ Hybrid least squares support vector machines for short term predictive analysis Zuriani, Mustaffa Ernawan, Ferda M. H., Sulaiman Syafiq Fauzi, Kamarulzaman QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering 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. Institute of Electrical and Electronics Engineers Inc. 2017 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17656/1/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/17656/7/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis%201.pdf 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 https://doi.org/10.1109/ICCAR.2017.7942762
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Zuriani, Mustaffa
Ernawan, Ferda
M. H., Sulaiman
Syafiq Fauzi, Kamarulzaman
Hybrid least squares support vector machines for short term predictive analysis
description 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.
format Conference or Workshop Item
author Zuriani, Mustaffa
Ernawan, Ferda
M. H., Sulaiman
Syafiq Fauzi, Kamarulzaman
author_facet Zuriani, Mustaffa
Ernawan, Ferda
M. H., Sulaiman
Syafiq Fauzi, Kamarulzaman
author_sort Zuriani, Mustaffa
title Hybrid least squares support vector machines for short term predictive analysis
title_short Hybrid least squares support vector machines for short term predictive analysis
title_full Hybrid least squares support vector machines for short term predictive analysis
title_fullStr Hybrid least squares support vector machines for short term predictive analysis
title_full_unstemmed Hybrid least squares support vector machines for short term predictive analysis
title_sort hybrid least squares support vector machines for short term predictive analysis
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/17656/
http://umpir.ump.edu.my/id/eprint/17656/
http://umpir.ump.edu.my/id/eprint/17656/1/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis.pdf
http://umpir.ump.edu.my/id/eprint/17656/7/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis%201.pdf
first_indexed 2023-09-18T22:24:31Z
last_indexed 2023-09-18T22:24:31Z
_version_ 1777415877300322304

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