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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2017
|
| Subjects: | |
| Online Access: | 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.pdf http://umpir.ump.edu.my/id/eprint/17656/ https://doi.org/10.1109/ICCAR.2017.7942762 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831524757272526848 |
|---|---|
| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.17656 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | eprints |
| spelling | my.ump.umpir.176562023-04-11T07:04:19Z 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 pdf en http://umpir.ump.edu.my/id/eprint/17656/1/Hybrid%20Least%20Squares%20Support%20Vector%20Machines%20for%20Short%20Term%20Predictive%20Analysis.pdf pdf en http://umpir.ump.edu.my/id/eprint/17656/7/Hybrid%20least%20squares%20support%20vector%20machines%20for%20short%20term%20predictive%20analysis.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 (Published) https://doi.org/10.1109/ICCAR.2017.7942762 |
| 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 |
| title | 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_short | Hybrid least squares support vector machines for short term predictive analysis |
| title_sort | hybrid least squares support vector machines for short term predictive analysis |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | 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.pdf http://umpir.ump.edu.my/id/eprint/17656/ https://doi.org/10.1109/ICCAR.2017.7942762 |
| url_provider | http://umpir.ump.edu.my/ |