A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

Shadmani, A; Nikoo, MR; Gandomi, AH; Wang, RQ; Golparvar, B

A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

Shadmani, A Nikoo, MR Gandomi, AH Wang, RQ Golparvar, B

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Energy Strategy Reviews, 2023, 49
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Shadmani, A
dc.contributor.author	Nikoo, MR
dc.contributor.author	Gandomi, AH
dc.contributor.author	Wang, RQ
dc.contributor.author	Golparvar, B
dc.date.accessioned	2024-03-21T05:35:57Z
dc.date.available	2024-03-21T05:35:57Z
dc.date.issued	2023-09-01
dc.identifier.citation	Energy Strategy Reviews, 2023, 49
dc.identifier.issn	2211-467X
dc.identifier.issn	2211-4688
dc.identifier.uri	http://hdl.handle.net/10453/176973
dc.description.abstract	Ocean energy technologies are in their developmental stages, like other renewable energy sources. To be useable in the energy market, most components of wave energy devices require further improvement. Additionally, wave resource characteristics must be evaluated and estimated correctly to assess the wave energy potential in various coastal areas. Multiple algorithms integrated with numerical models have recently been developed and utilized to estimate, predict, and forecast wave characteristics and wave energy resources. Each algorithm is vital in designing wave energy converters (WECs) to harvest more energy. Although several algorithms based on optimization approaches have been developed for efficiently designing WECs, they are unreliable and suffer from high computational costs. To this end, novel algorithms incorporating machine learning and deep learning have been presented to forecast wave energy resources and optimize WEC design. This review aims to classify and discuss the key characteristics of machine learning and deep learning algorithms that apply to wave energy forecast and optimal configuration of WECs. Consequently, in terms of convergence rate, combining optimization methods, machine learning, and deep learning algorithms can improve the WECs configuration and wave characteristic forecasting and optimization. In addition, the high capability of learning algorithms for forecasting wave resource and energy characteristics was emphasized. Moreover, a review of power take-off (PTO) coefficients and the control of WECs demonstrated the indispensable ability of learning algorithms to optimize PTO parameters and the design of WECs.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Energy Strategy Reviews
dc.relation.isbasedon	10.1016/j.esr.2023.101180
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1402 Applied Economics, 1605 Policy and Administration
dc.title	A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization
dc.type	Journal Article
utslib.citation.volume	49
utslib.for	1402 Applied Economics
utslib.for	1605 Policy and Administration
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-21T05:35:54Z
pubs.publication-status	Published
pubs.volume	49

Abstract:

Ocean energy technologies are in their developmental stages, like other renewable energy sources. To be useable in the energy market, most components of wave energy devices require further improvement. Additionally, wave resource characteristics must be evaluated and estimated correctly to assess the wave energy potential in various coastal areas. Multiple algorithms integrated with numerical models have recently been developed and utilized to estimate, predict, and forecast wave characteristics and wave energy resources. Each algorithm is vital in designing wave energy converters (WECs) to harvest more energy. Although several algorithms based on optimization approaches have been developed for efficiently designing WECs, they are unreliable and suffer from high computational costs. To this end, novel algorithms incorporating machine learning and deep learning have been presented to forecast wave energy resources and optimize WEC design. This review aims to classify and discuss the key characteristics of machine learning and deep learning algorithms that apply to wave energy forecast and optimal configuration of WECs. Consequently, in terms of convergence rate, combining optimization methods, machine learning, and deep learning algorithms can improve the WECs configuration and wave characteristic forecasting and optimization. In addition, the high capability of learning algorithms for forecasting wave resource and energy characteristics was emphasized. Moreover, a review of power take-off (PTO) coefficients and the control of WECs demonstrated the indispensable ability of learning algorithms to optimize PTO parameters and the design of WECs.

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http://hdl.handle.net/10453/176973