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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Main Authors:	Zuriani, Mustaffa, M. H., Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman
Format:	Article
Language:	English
Published:	United Kingdom Simulation Society 2016
Subjects:	Q Science (General) QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/1/peoco2016_IJSSST_0035.pdf

id	ump-16362
recordtype	eprints
spelling	ump-163622018-02-26T07:50:20Z http://umpir.ump.edu.my/id/eprint/16362/ Hybrid Metaheuristic Algorithm for Short Term Load Forecasting Zuriani, Mustaffa M. H., Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman Q Science (General) QA75 Electronic computers. Computer science 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. United Kingdom Simulation Society 2016 Article NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16362/1/peoco2016_IJSSST_0035.pdf Zuriani, Mustaffa and M. H., Sulaiman and Yuhanis, Yusof and Syafiq Fauzi, Kamarulzaman (2016) Hybrid Metaheuristic Algorithm for Short Term Load Forecasting. International Journal of Simulation: Systems, Science & Technology (IJSSST), 17 (41). pp. 1-6. ISSN 1473-8031 (print); 1473-804x (online) http://ijssst.info/Vol-17/No-41/paper6.pdf doi: 10.5013/IJSS ST.a.15.01.03
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General) QA75 Electronic computers. Computer science
spellingShingle	Q Science (General) QA75 Electronic computers. Computer science Zuriani, Mustaffa M. H., Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
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.
format	Article
author	Zuriani, Mustaffa M. H., Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman
author_facet	Zuriani, Mustaffa M. H., Sulaiman Yuhanis, Yusof Syafiq Fauzi, Kamarulzaman
author_sort	Zuriani, Mustaffa
title	Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
title_short	Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
title_full	Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
title_fullStr	Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
title_full_unstemmed	Hybrid Metaheuristic Algorithm for Short Term Load Forecasting
title_sort	hybrid metaheuristic algorithm for short term load forecasting
publisher	United Kingdom Simulation Society
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/ http://umpir.ump.edu.my/id/eprint/16362/1/peoco2016_IJSSST_0035.pdf
first_indexed	2023-09-18T22:21:58Z
last_indexed	2023-09-18T22:21:58Z
_version_	1777415716300914688

Hybrid Metaheuristic Algorithm for Short Term Load Forecasting

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