Test environment running 7.6.6

Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Long-Short Term Memory Based TALOS Wave Energy Converter Power Output Prediction with Numerical Modelling

Thumbnail Image

Date

2023

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Wave energy shows potential to provide electricity in a renewable manner. The TALOS WEC (Wave Energy Converter) is a unique design with six PTO (Power Take-Off) elements to provide six Degrees of Freedom (DOFs), which is potentially able to harvest energy more efficiently than traditional single-DOF devices. As a step towards its optimisation and control, a power prediction model is developed, using the wave elevation and motions of the WEC to predict the power output of each PTO. The results show that using LSTM (Long-Short Term Memory) has a higher prediction accuracy than the other approaches considered.

Description

Keywords

LSTM, machine learning, power prediction, TALOS, WEC

Citation

URI

https://dspace-test.anu.edu.au/handle/1885/733742732

Collections

DevOps Pure Testing

Source

Type

Conference contribution

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

DSpace software copyright © 2002-2026 LYRASIS