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Application of Artificial Intelligence Methods for Hybrid Energy System Optimization

Consciousness of the need to decrease our unnatural weather changes and of the critical increase in the costs of traditional sources of energy have motivated many nations to provide innovative energy strategies that promulgate renewable energy systems. For example, solar, wind and hydro related ener...

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Main Authors: Zahraee, S.M., Khalaji Assadi, M., Saidur, R.
Format: Article
Published: Elsevier Ltd 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983732554&doi=10.1016%2fj.rser.2016.08.028&partnerID=40&md5=f12ff7d8df014878549553fb0c161146
http://eprints.utp.edu.my/30980/
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spelling my.utp.eprints.309802022-03-25T07:52:34Z Application of Artificial Intelligence Methods for Hybrid Energy System Optimization Zahraee, S.M. Khalaji Assadi, M. Saidur, R. Consciousness of the need to decrease our unnatural weather changes and of the critical increase in the costs of traditional sources of energy have motivated many nations to provide innovative energy strategies that promulgate renewable energy systems. For example, solar, wind and hydro related energies are renewable energy sources, and they are environmentally friendly with the potential for broad use. All of the load requirement conditions in comparison with single usage can provide more economical and dependable electricity, as well as environmentally friendly sources, by compounding such renewable energy sources using backup units to shape a hybrid scheme. Sizing the hybrid system elements optimally is one of the most important matters in this type of hybrid system, which could sufficiently meet all of the load demands with a minor financial investment. Although a number of studies have been performed on the optimization and sizing of hybrid renewable energy systems, this study presents a full analysis of Artificial Intelligence optimum plans in the literature, making the contribution of penetrating extensively the renewable energy aspects for improving the functioning of the systems economically. © 2016 Elsevier Ltd Elsevier Ltd 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983732554&doi=10.1016%2fj.rser.2016.08.028&partnerID=40&md5=f12ff7d8df014878549553fb0c161146 Zahraee, S.M. and Khalaji Assadi, M. and Saidur, R. (2016) Application of Artificial Intelligence Methods for Hybrid Energy System Optimization. Renewable and Sustainable Energy Reviews, 66 . pp. 617-630. http://eprints.utp.edu.my/30980/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Consciousness of the need to decrease our unnatural weather changes and of the critical increase in the costs of traditional sources of energy have motivated many nations to provide innovative energy strategies that promulgate renewable energy systems. For example, solar, wind and hydro related energies are renewable energy sources, and they are environmentally friendly with the potential for broad use. All of the load requirement conditions in comparison with single usage can provide more economical and dependable electricity, as well as environmentally friendly sources, by compounding such renewable energy sources using backup units to shape a hybrid scheme. Sizing the hybrid system elements optimally is one of the most important matters in this type of hybrid system, which could sufficiently meet all of the load demands with a minor financial investment. Although a number of studies have been performed on the optimization and sizing of hybrid renewable energy systems, this study presents a full analysis of Artificial Intelligence optimum plans in the literature, making the contribution of penetrating extensively the renewable energy aspects for improving the functioning of the systems economically. © 2016 Elsevier Ltd
format Article
author Zahraee, S.M.
Khalaji Assadi, M.
Saidur, R.
spellingShingle Zahraee, S.M.
Khalaji Assadi, M.
Saidur, R.
Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
author_facet Zahraee, S.M.
Khalaji Assadi, M.
Saidur, R.
author_sort Zahraee, S.M.
title Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
title_short Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
title_full Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
title_fullStr Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
title_full_unstemmed Application of Artificial Intelligence Methods for Hybrid Energy System Optimization
title_sort application of artificial intelligence methods for hybrid energy system optimization
publisher Elsevier Ltd
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983732554&doi=10.1016%2fj.rser.2016.08.028&partnerID=40&md5=f12ff7d8df014878549553fb0c161146
http://eprints.utp.edu.my/30980/
_version_ 1738657183609913344
score 13.211869

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