# Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

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## Abstract

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## 1. Introduction

- The numerical analysis of a Searaser in the form of the computational fluid dynamics is proposed by Flow-3D software, which completely demonstrates the ocean wave parameters and perfectly combines with the latest algorithm of long short-term memory.
- The artificial intelligence model is reasonably utilized to predict output electrical power based on a wind flow speed, and a mathematical relation between wave height and output power can be obtained to help the WEC industry and investors to predict output power, thus saving time and cost.

## 2. Materials and Methods

#### 2.1. Dataset

#### 2.2. Geometry and Description

#### 2.3. Governing Equations

_{G}is the velocity of center of mass. Simplification of Equation (1) was done by assuming the fact that the buoy movement is divided into two main parts; rotational and translational with total 6 degrees of freedom. Hence, by assuming the buoy as a rigid body, all of the movement takes place in the center of mass. In this study, the “o” index introduced the buoy center of mass. Equation (3) shows the buoy movement velocity [27].

#### 2.4. Boundary Conditions and Grid Generation

#### 2.5. Machine Learning LSTM Method of Prediction

_{t}

_{−1}introduces the output vector of the last time step; and X

_{t}shows the input vector of the current time step.

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 8.**The linearization of scatter data within the correlation of different parameters with each other.

Title | 1 | 2 | 3 |
---|---|---|---|

Mesh block (Number 1) | |||

Total number of elements | 7,000,000 | 5,000,000 | 1,000,000 |

Run time | 5 days 7 h | 4 days 1 h | 2 days 20 h |

The accuracy of Searaser displacement parameter | 95% | 93% | 85% |

LSTM Parameters | Recent Study Parameters |
---|---|

${C}_{t-1}$ | Input parameters 1. Wave height 2. Time 3. Wind slow velocity 4. Number |

${h}_{t}$ | Output parameters 1. Generated power |

Analysis Variables | RSME Value |
---|---|

Power output, wave height | 0.56 |

Power output, simulation time | 0.42 |

Power output, wave height, simulation time | 0.49 |

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**MDPI and ACS Style**

Mousavi, S.M.; Ghasemi, M.; Dehghan Manshadi, M.; Mosavi, A.
Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory. *Mathematics* **2021**, *9*, 871.
https://doi.org/10.3390/math9080871

**AMA Style**

Mousavi SM, Ghasemi M, Dehghan Manshadi M, Mosavi A.
Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory. *Mathematics*. 2021; 9(8):871.
https://doi.org/10.3390/math9080871

**Chicago/Turabian Style**

Mousavi, Seyed Milad, Majid Ghasemi, Mahsa Dehghan Manshadi, and Amir Mosavi.
2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory" *Mathematics* 9, no. 8: 871.
https://doi.org/10.3390/math9080871