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)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Grey wolf optimizer; Least squares support vector machines; Load forecasting; Metaheuristic algorithm; Optimization |
| 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 Norsaini Abdul Samat |
| Date Deposited: | 18 Aug 2022 07:09 |
| Last Modified: | 18 Aug 2022 07:09 |
| URI: | http://umpir.ump.edu.my/id/eprint/30099 |
| Download Statistic: | View Download Statistics |
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