Open AccessData‐driven models for short‐term ocean wave power forecasting
Author(s) -
Ni Chenhua
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12157
Subject(s) - support vector machine , artificial neural network , term (time) , computer science , power (physics) , data modeling , artificial intelligence , power grid , recurrent neural network , machine learning , data mining , physics , quantum mechanics , database
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.