Data‐driven models for short‐term ocean wave power forecasting

Autor:	Chenhua Ni
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Tidal and flow energy Surface waves tides and sea level Instrumentation and techniques for geophysical hydrospheric and lower atmosphere research Wave power Renewable energy sources TJ807-830
Zdroj:	IET Renewable Power Generation, Vol 15, Iss 10, Pp 2228-2236 (2021)
Druh dokumentu:	article
ISSN:	1752-1424 1752-1416
DOI:	10.1049/rpg2.12157
Popis:	Abstract In order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested using data from an oscillating body converter. Several other methods are tested as well. These include support vector machines (SVM), neural networks (NN), and recurrent neural networks (RNN). Of these, the best is the long‐short‐term memory (LSTM) network, which is trained and updated on observed values. The experiments demonstrate both the SVM and NN forecast well. However, the proposed deep learning models predict them more accurately. The models work well over short horizons. At horizons longer than three days, accuracy deteriorates, and the models cannot fit the data well.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/23a9489f0a3543cf9ac783b4790344d0 Zobrazit plný text záznamu Plný text View record in DOAJ