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

Journal Article

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

Author:

Shadmani, A.; Nikoo, M.; Gandomi, A.; Wang, R-Q.; Golparvar, B.

Publication Date:

September 1, 2023

Journal:

Energy Strategy Reviews

Volume:

Publisher:

Elsevier

Affiliation:

Amirkabir University of Technology, Ghent University, Sultan Qaboos University, University of Technology Sydney, Obuda University, Rutgers University

Technology:

Wave

Engineering:

Power Take Off

Document Access

Website:

External Link

Attachment:

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Citation

Shadmani, A.; Nikoo, M.; Gandomi, A.; Wang, R-Q.; Golparvar, B. (2023). A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization. Energy Strategy Reviews, 49 https://doi.org/10.1016/j.esr.2023.101180

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.