A novel hybrid metaheuristic algorithm for short term load forecasting
Electric load forecasting is undeniably a demanding business due to its complexity and high nonlinearity features. It is regarded as vital in electricity industry and critical for the party of interest as it provides useful support in power system management. Despite the aforementioned situation, a...
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| Format: | Article |
| Language: | English |
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UK Simulation Society
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30099/ http://umpir.ump.edu.my/id/eprint/30099/1/A%20novel%20hybrid%20metaheuristic%20algorithm%20for%20short%20term%20load%20forecasting.pdf |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Electric load forecasting is undeniably a demanding business due to its complexity and high nonlinearity features. It is regarded as vital in electricity industry and critical for the party of interest as it provides useful support in power system management. Despite the aforementioned situation, a reliable forecasting accuracy is essential for efficient future planning and maximize the profits of stakeholders as well. With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). Act as an optimization tool for LSSVM hyper parameters, the inducing of GWO assists the LSSVM in achieving optimality, hence good generalization in forecasting can be achieved. Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). Findings of the study revealed that, by producing lower Root Mean Square Percentage Error (RMSPE), the GWO-LSSVM is able to outperform the identified algorithms for the data set of interest. |
| first_indexed | 2025-11-15T02:57:04Z |
| format | Article |
| id | ump-30099 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:57:04Z |
| publishDate | 2017 |
| publisher | UK Simulation Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-300992022-08-18T07:09:46Z http://umpir.ump.edu.my/id/eprint/30099/ A novel hybrid metaheuristic algorithm for short term load forecasting Zuriani, Mustaffa Mohd Herwan, Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Electric load forecasting is undeniably a demanding business due to its complexity and high nonlinearity features. It is regarded as vital in electricity industry and critical for the party of interest as it provides useful support in power system management. Despite the aforementioned situation, a reliable forecasting accuracy is essential for efficient future planning and maximize the profits of stakeholders as well. With respect to that matter, this study presents a hybrid Least Squares Support Vector Machines (LSSVM) with a rather new Swarm Intelligence (SI) algorithm namely Grey Wolf Optimizer (GWO). Act as an optimization tool for LSSVM hyper parameters, the inducing of GWO assists the LSSVM in achieving optimality, hence good generalization in forecasting can be achieved. Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). Findings of the study revealed that, by producing lower Root Mean Square Percentage Error (RMSPE), the GWO-LSSVM is able to outperform the identified algorithms for the data set of interest. UK Simulation Society 2017 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30099/1/A%20novel%20hybrid%20metaheuristic%20algorithm%20for%20short%20term%20load%20forecasting.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Yuhanis, Yusof and Syafiq Fauzi, Kamarulzaman (2017) A novel hybrid metaheuristic algorithm for short term load forecasting. International Journal of Simulation: Systems, Science and Technology, 17 (41). 6.1-6.6. ISSN 1473-804x(Online); 1473-8031(print). (Published) https://ijssst.info/Vol-17/No-41/paper6.pdf |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman A novel hybrid metaheuristic algorithm for short term load forecasting |
| title | A novel hybrid metaheuristic algorithm for short term load forecasting |
| title_full | A novel hybrid metaheuristic algorithm for short term load forecasting |
| title_fullStr | A novel hybrid metaheuristic algorithm for short term load forecasting |
| title_full_unstemmed | A novel hybrid metaheuristic algorithm for short term load forecasting |
| title_short | A novel hybrid metaheuristic algorithm for short term load forecasting |
| title_sort | novel hybrid metaheuristic algorithm for short term load forecasting |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30099/ http://umpir.ump.edu.my/id/eprint/30099/ http://umpir.ump.edu.my/id/eprint/30099/1/A%20novel%20hybrid%20metaheuristic%20algorithm%20for%20short%20term%20load%20forecasting.pdf |