A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Salcedo-Sanz, S. | - |
dc.contributor.author | Pastor-Sanchez, A. | - |
dc.contributor.author | Del Ser, J. | - |
dc.contributor.author | Prieto, L. | - |
dc.contributor.author | Geem, Z. W. | - |
dc.date.available | 2020-02-28T09:46:58Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2015-03 | - |
dc.identifier.issn | 0960-1481 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/10715 | - |
dc.description.abstract | This paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bioinspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to na ve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines. (C) 2014 Elsevier Ltd. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | RENEWABLE ENERGY | - |
dc.subject | ARTIFICIAL NEURAL-NETWORKS | - |
dc.subject | EXTREME LEARNING-MACHINE | - |
dc.subject | SUPPORT VECTOR MACHINES | - |
dc.subject | SHORT-TERM PREDICTION | - |
dc.subject | MODELS | - |
dc.subject | REGRESSION | - |
dc.subject | DESIGN | - |
dc.subject | BANKS | - |
dc.title | A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000347580600011 | - |
dc.identifier.doi | 10.1016/j.renene.2014.09.027 | - |
dc.identifier.bibliographicCitation | RENEWABLE ENERGY, v.75, pp.93 - 101 | - |
dc.identifier.scopusid | 2-s2.0-84908046994 | - |
dc.citation.endPage | 101 | - |
dc.citation.startPage | 93 | - |
dc.citation.title | RENEWABLE ENERGY | - |
dc.citation.volume | 75 | - |
dc.contributor.affiliatedAuthor | Geem, Z. W. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Short term wind speed prediction | - |
dc.subject.keywordAuthor | Feature selection | - |
dc.subject.keywordAuthor | Coral Reefs Optimization | - |
dc.subject.keywordAuthor | Harmony Search | - |
dc.subject.keywordAuthor | Extreme learning machines | - |
dc.subject.keywordPlus | ARTIFICIAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | EXTREME LEARNING-MACHINE | - |
dc.subject.keywordPlus | SUPPORT VECTOR MACHINES | - |
dc.subject.keywordPlus | SHORT-TERM PREDICTION | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | BANKS | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Energy & Fuels | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Energy & Fuels | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.