Journal of Guangxi Normal University(Natural Science Edition) ›› 2020, Vol. 38 ›› Issue (3): 25-32.doi: 10.16088/j.issn.1001-6600.2020.03.004

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Wave Power Prediction Based on Deep Learning

ZHANG Mingyu1,ZHAO Meng1*,CAI Fuhong1,LIANG Yu2,WANG Xinhong3   

  1. 1. Mechanical and Electrical Engineering College, Hainan University,Haikou Hainan 570228,China;
    2. Electric Power Research Institute of Hainan Power Grid Corporation Ltd, Haikou Hainan 570105,China;
    3. School of Civil Engineering and Architecture,Northeast Electric Power University,Jilin Jilin 132012,China
  • Received:2019-06-28 Online:2020-05-25 Published:2020-06-11

Abstract: In view of the instability of the output power caused by the strong randomness of wave height and frequency in wave energy generation, it’s proposed to use the prediction data of wave energy generation power to assist the accurate operation of energy storage system (physical energy storage and chemical energy storage) to stabilize its volatility. The prediction data is predicted based on the wave energy generation power prediction method combining with the Long-Short Term Memory (LSTM) and the Back Propagation Neural Network (BP). The experiment is conducted by using two-year weather data and 245-day wave energy generation power data from an island in the South China Sea, and three predicted time span LSTM-BP models are trained and tested. The prediction results of the power of a wave energy generation boat are given at different time spans. It’s showed that the LSTM-BP model can be used to predict the output power of wave energy generation well.

Key words: power prediction, wave energy, deep learning, LSTM-BP

CLC Number: 

  • TM743
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