Maximization of energy absorption for a wave energy converter using the deep machine learning

Liang Li, Zhiming Yuan, Yan Gao

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)
143 Downloads (Pure)


A controller is usually used to maximize the energy absorption of wave energy converter. Despite the development of various control strategies, the practical implementation of wave energy control is still difficult since the control inputs are the future wave forces. In this work, the artificial intelligence technique is adopted to tackle this problem. A multi-layer artificial neural network is developed and trained by the deep machine learning algorithm to forecast the short-term wave forces. The model predictive control strategy is used to implement real-time latching control action to a heaving point-absorber. Simulation results show that the average energy absorption is increased substantially with the controller. Since the future wave forces are predicted, the controller is applicable to a full-scale wave energy converter in practice. Further analysis indicates that the prediction error has a negative effect on the control performance, leading to the reduction of energy absorption.

Original languageEnglish
Pages (from-to)340-349
Number of pages10
Issue numberPart A
Early online date18 Sept 2018
Publication statusPublished - 15 Dec 2018


  • wave energy converter
  • wave energy control
  • energy absorption
  • neural network
  • deep machine learning
  • wave force prediction


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