Open AccessShort-term tidal current prediction based on GA-BP neural network
Author(s) -
Xiangshuo Qiao,
Fengyi Guo,
Runfeng Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/513/1/012061
Subject(s) - current (fluid) , artificial neural network , tidal current , term (time) , tidal power , computer science , renewable energy , field (mathematics) , long term prediction , machine learning , engineering , geology , marine engineering , telecommunications , mathematics , oceanography , electrical engineering , physics , quantum mechanics , pure mathematics
Tidal current is a novel type of renewable energy for power supply. Accurate and stable tidal current prediction is an important research area in the field of tidal current energy development. In this paper, the GA-BP Neural Network is studied deeply, and the historical time series data and time factor are adopted to improve the input. Besides, the prediction model of tidal current components is established based on the experiment data. To validate the effectiveness of the presented method, other five popular prediction models are also introduced and compared. The simulation results show that the model established in this paper is superior to other five models and has better prediction ability in terms of two commonly used performance indices.