Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site

The hike in fossil-fuel prices and increasing environmental damage due to the subsequent Carbon Monoxide (CO) emission from burning fossil-fuel is becoming a major concern for every nation. The possibility of generating power from natural sources such as solar, wind, and sea waves is thus considered...

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Main Authors: Umair, M., Hashmani, M.A., Keiichi, H.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29884 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097560138&doi=10.1109%2fICCI51257.2020.9247677&partnerID=40&md5=95f609b6ce635ad28b1de7e44d093fe2
http://eprints.utp.edu.my/29884/
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id utp-eprints.29884
recordtype eprints
spelling utp-eprints.298842022-03-25T03:05:26Z Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site Umair, M. Hashmani, M.A. Keiichi, H. The hike in fossil-fuel prices and increasing environmental damage due to the subsequent Carbon Monoxide (CO) emission from burning fossil-fuel is becoming a major concern for every nation. The possibility of generating power from natural sources such as solar, wind, and sea waves is thus considered as an alternative. In the case of the sea waves, the kinetic energy of surface waves can be converted into single direction motion which runs a turbine to generate electricity. A Wave Energy Converter (WEC) is such an installation that converts the wave energy into electrical energy. In this study, we have conducted a literature investigation to identify the significant meteorological and wind-wave data parameters which determine wave-energy potential at a wave energy converter site and then identified optimal feature sets from buoy data for machine prediction of those identified parameters. The authors hope that by suggesting optimal feature sets, the outcomes of this study will help in improving the computational efficiency of machine learning models specially designed for wave parameter prediction at WEC sites. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097560138&doi=10.1109%2fICCI51257.2020.9247677&partnerID=40&md5=95f609b6ce635ad28b1de7e44d093fe2 Umair, M. and Hashmani, M.A. and Keiichi, H. (2020) Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site. In: UNSPECIFIED. http://eprints.utp.edu.my/29884/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The hike in fossil-fuel prices and increasing environmental damage due to the subsequent Carbon Monoxide (CO) emission from burning fossil-fuel is becoming a major concern for every nation. The possibility of generating power from natural sources such as solar, wind, and sea waves is thus considered as an alternative. In the case of the sea waves, the kinetic energy of surface waves can be converted into single direction motion which runs a turbine to generate electricity. A Wave Energy Converter (WEC) is such an installation that converts the wave energy into electrical energy. In this study, we have conducted a literature investigation to identify the significant meteorological and wind-wave data parameters which determine wave-energy potential at a wave energy converter site and then identified optimal feature sets from buoy data for machine prediction of those identified parameters. The authors hope that by suggesting optimal feature sets, the outcomes of this study will help in improving the computational efficiency of machine learning models specially designed for wave parameter prediction at WEC sites. © 2020 IEEE.
format Conference or Workshop Item
author Umair, M.
Hashmani, M.A.
Keiichi, H.
spellingShingle Umair, M.
Hashmani, M.A.
Keiichi, H.
Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
author_sort Umair, M.
title Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
title_short Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
title_full Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
title_fullStr Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
title_full_unstemmed Optimal Feature Identification for Machine Prediction of Wind-Wave Parameters at Wave Energy Converter Site
title_sort optimal feature identification for machine prediction of wind-wave parameters at wave energy converter site
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097560138&doi=10.1109%2fICCI51257.2020.9247677&partnerID=40&md5=95f609b6ce635ad28b1de7e44d093fe2
http://eprints.utp.edu.my/29884/
_version_ 1741197314549088256
score 11.62408

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