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A hybrid metaheuritic technique developed for hourly load forecasting

Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and...

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Main Authors: Mahrami, M., Rahmani, R., Seyedmahmoudian, M., Mashayekhi, R., Karimi, H., Hosseini, E.
Format: Article
Language:English
Published: John Wiley and Sons Inc. 2016
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/71567/1/HediyehKarimi2016_AHybridMetaheuriticTechniqueDeveloped.pdf
http://eprints.utm.my/id/eprint/71567/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959869181&doi=10.1002%2fcplx.21766&partnerID=40&md5=fccb5fabaf4ac22ee664893d05708860
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spelling my.utm.715672017-11-20T08:28:24Z http://eprints.utm.my/id/eprint/71567/ A hybrid metaheuritic technique developed for hourly load forecasting Mahrami, M. Rahmani, R. Seyedmahmoudian, M. Mashayekhi, R. Karimi, H. Hosseini, E. TA Engineering (General). Civil engineering (General) Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short-term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two-stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems. John Wiley and Sons Inc. 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/71567/1/HediyehKarimi2016_AHybridMetaheuriticTechniqueDeveloped.pdf Mahrami, M. and Rahmani, R. and Seyedmahmoudian, M. and Mashayekhi, R. and Karimi, H. and Hosseini, E. (2016) A hybrid metaheuritic technique developed for hourly load forecasting. Complexity, 21 . pp. 521-532. ISSN 1076-2787 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959869181&doi=10.1002%2fcplx.21766&partnerID=40&md5=fccb5fabaf4ac22ee664893d05708860
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Mahrami, M.
Rahmani, R.
Seyedmahmoudian, M.
Mashayekhi, R.
Karimi, H.
Hosseini, E.
A hybrid metaheuritic technique developed for hourly load forecasting
description Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short-term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two-stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems.
format Article
author Mahrami, M.
Rahmani, R.
Seyedmahmoudian, M.
Mashayekhi, R.
Karimi, H.
Hosseini, E.
author_facet Mahrami, M.
Rahmani, R.
Seyedmahmoudian, M.
Mashayekhi, R.
Karimi, H.
Hosseini, E.
author_sort Mahrami, M.
title A hybrid metaheuritic technique developed for hourly load forecasting
title_short A hybrid metaheuritic technique developed for hourly load forecasting
title_full A hybrid metaheuritic technique developed for hourly load forecasting
title_fullStr A hybrid metaheuritic technique developed for hourly load forecasting
title_full_unstemmed A hybrid metaheuritic technique developed for hourly load forecasting
title_sort hybrid metaheuritic technique developed for hourly load forecasting
publisher John Wiley and Sons Inc.
publishDate 2016
url http://eprints.utm.my/id/eprint/71567/1/HediyehKarimi2016_AHybridMetaheuriticTechniqueDeveloped.pdf
http://eprints.utm.my/id/eprint/71567/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959869181&doi=10.1002%2fcplx.21766&partnerID=40&md5=fccb5fabaf4ac22ee664893d05708860
_version_ 1643656218037714944
score 13.211869

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