# Review on Deep Learning Research and Applications in Wind and Wave Energy

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Deep Learning Applications of Wind and Wave Energy

#### 2.1. Forecasting of Wind and Wave Energy

#### 2.1.1. Differences on Datasets Used

#### 2.1.2. Preprocessing

#### 2.1.3. Evaluation and Comparison Methods

#### 2.2. Optimization Application on Wind and Wave Energy

#### 2.3. Pattern Recognition and Correlations Identification

_{i}and Y

_{i}are individual variables, and $\overline{{\mathrm{X}}_{\mathrm{i}}}$ and $\overline{{\mathrm{Y}}_{\mathrm{i}}}$ are the mean value of the individual parameters.

## 3. Challenges and Future Research Directions

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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Applications |
---|

Wave height (Buoy), wind speed [38] |

Wave height/period, wind speed/direction, sea level pressure, gust speed, air pressure, Sea surface temperature, buoy data [39] |

Mean wave period (wave buoy data) [37] |

Offshore wind speed (light detection and ranging and seashore meteorological mast) [40] |

Wave height/period/direction (buoy station from NOAA) [41] |

Daily ocean wave height prediction [42] |

Wind power generation [43] |

Wind power forecast [44] |

Wind speed forecasting [45,46,47,48] |

Wind forecasting [49] |

Wind farm cluster power prediction [50] |

Surface wind forecast [51] |

Application | Time Step | Location | Model Used | Type | Pre-Processing |
---|---|---|---|---|---|

Wave conditions, wind velocity [55] | s | Gulf of Lion in the north-western Mediterranean Sea | LSTM | - | - |

Wave height, Wind speed [57] | s | The East China Sea; The Yellow Sea | BiGRU | short term 3–24 h | - |

Wave and wind conditions [39] | min | Gulf of Mexico; North Atlantic | LSTM/RNN | Long term 2-day | - |

Mean wave period [62] | 3-h | Coast of central America | DNN | Short-term | Standard deviation |

Offshore wind speed [40] | 10-min | Gulf of Khambat; Gulf of Mannar | CNN; LSTM; Bidirectional LSTM; | Short term | EEMD |

Wave conditions [41] | 1-h | U.S. Atlantic coast | LSTM | 1–48 h | Standard |

Daily ocean wave height [42] | sec | Gulf of Mexico; Korean region; UK region | Sequential learning neural networks | - | ELM |

Significant wave height [63] | 1-h | Three buoy stations | 1-dimentional-CNN-position encoding | 6–24 h | STL |

Wave energy period [64] | Half hour | Queensland, Australia | CNN + RNN | - | ELM |

Ref. | Time Step | Location | Model Used | Type | Pre-Processing |
---|---|---|---|---|---|

[29] | 2 h | Onshore | CNN + LSTM | 1–3 h | ELM |

[30] | 10-min/1 h | Onshore | LSTMDE-HELM | Short term | DE |

[38] | hourly | Onshore/coast | CNN + GRU | 1–6 h | - |

[45] | - | Onshore | hybrid LSTM + DBN | Short term | Singular spectrum decomposition |

[46] | 10-min | Offshore | Negative correlation learning | Short term | OVMD |

[47] | - | Offshore | BiLSTM | - | - |

[48] | 10-min | Onshore/coast | MLP, LSTM, ARIMA | - | EMD |

[49] | 24-h | Onshore | CNN | Short term | - |

[53] | hourly | Coast/offshore | DNN | 1–12 h | - |

[60] | min | Onshore | SC-LSTM | 37 days | WCT |

[61] | monthly/seasonal/annual | Onshore/offshore | STSR-LSTM | Long term | - |

[65] | 1–2 h | Onshore | QRNN | Short term | Signal reconstruction decomposed |

[66] | 1-h | Onshore | CNN-LSTM | - | EWT |

[67] | 1–5 months | onshore | RNNs + SVM | 6–24 h | WT |

[68] | 5-min | Onshore | Multivariable stacked long-short term memory network | Ultra short term | Normalization |

[69] | hourly | Onshore | Causal convolutional gated recurrent unit | Short term | Multiple decomposition |

[70] | - | Onshore | Deep feature extraction + LSTM | Short term | Batch normalization |

[71] | 1-h | Onshore | Wavelet neural network EEMD-AWNN | - | Ensemble empirical mode decomposition |

[72] | daily | Offshore | LSTM + STCG | - | STCG |

[73] | 30–60 days | Coast/offshore | Data area division + LSTM | Short term | CEEMD |

Applications | Time Step | Location | Model Used | Type | Pre-Processing |
---|---|---|---|---|---|

Wind power [33] | 15-min | Onshore | BiGRU | Ultra short term | ECM |

Wind speed/turbulent [66] | 4 h | Onshore | 1-D CNN | 24 h, Real-time | Short term |

Wind power [74] | 10/30/60/20-min | Onshore | Stacked ensemble learning | Short term/very short term | ECM |

Thunderstorm [75] | 91-min | Onshore | 1-D CNN | Short term | - |

Wind turbine data uncertainty [76] | Onshore | Hybrid RNN-LSTM | Fuzzy based | ||

Wind power [43] | 24 h | - | LSTM-GMM | Short term | Gaussian mixture model |

Wind power [44] | - | Coast | LSTM + LUBE | Short term | LUBE |

Wind farm cluster power [50] | - | Onshore | CNN + LTSM | SSM | |

Surface wind [51] | 12 h | River | Real time 4D assimilation | Short term | - |

Model | Pros | Cons | Application |
---|---|---|---|

CNN | Feature extraction; ability to develop internal representation of two-dimensional image | Not store past sequences of patterns; Predetermined dataset | Image classification; object detection; image segmentation; classification of spatial data |

RNN | Processes time-series data in the short term | Limited performance in the short term | Time series data identification and forecasting |

LSTM | Processes long-term time series data | Complexity; | Long-term time series forecasting, prediction; pattern recognition |

GRU | Less memory than LSTM, simple design, faster; handles long-term data; mitigates the vanishing gradient problem | Low learning efficiency; slow convergence | Time series foresting model; wind speed forecasting; error model |

DBN | Good at unsupervised feature extraction | Cannot process meteorological dataset with multidimension | Single variables forecasting |

Hybrid | Flexibility, excellence in extracting different types of data; higher accuracy | Computational complexity; | Wind speed/power, wave scenario forecasting; typhoon/hurricane forecasting; power system optimization; energy storage size optimization |

Indicator | Equation |
---|---|

Mean absolute error (MAE) | $\mathrm{MAE}=\frac{1}{\mathrm{N}}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{N}}}|{\mathrm{X}}_{\mathrm{i}}-\widehat{{\mathrm{X}}_{\mathrm{i}}}|$ |

Mean absolute percentage error (MAPE) | $\mathrm{MAPE}=\frac{1}{\mathrm{N}}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{N}}}|\frac{{\mathrm{X}}_{\mathrm{i}}-\widehat{{\mathrm{X}}_{\mathrm{i}}}}{{\mathrm{X}}_{\mathrm{i}}}|$ |

Root mean square error (RMSE) | $\mathrm{RMSE}=\sqrt{\frac{1}{\mathrm{N}}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{N}}}{\left({\mathrm{X}}_{\mathrm{i}}-\widehat{{\mathrm{X}}_{\mathrm{i}}}\right)}^{2}}$ |

Coefficient of determination (R^{2}) | ${\mathrm{R}}^{2}=1-\frac{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{i}}-\widehat{{\mathrm{X}}_{\mathrm{i}}}\right)}^{2}}{{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{i}}-\overline{{\mathrm{X}}_{\mathrm{i}}}\right)}^{2}}$ |

Mean absolute percentage error (MAPE) | $\mathrm{MAPE}=\frac{1}{\mathrm{N}}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{N}}}|\frac{{\mathrm{X}}_{\mathrm{i}}-\widehat{{\mathrm{X}}_{\mathrm{i}}}}{{\mathrm{X}}_{\mathrm{i}}}|\times 100\%$ |

Scatter index | $\mathrm{SI}=\frac{\mathrm{RMSE}}{\overline{{\mathrm{X}}_{\mathrm{i}}}}$ |

Forecasting Application | MAE | RMSE | R^{2} | MAPE | SI | Models | Lead Time |
---|---|---|---|---|---|---|---|

Wind speed [59] | 0.28 | 0.49 | 0.257 | SC-LSTM | |||

Wind speed [66] | 0.3 | 0.3925 | 0.4285 | CNN-LSTM | |||

Wind speed [63] | 0.07988 | 0.1052 | 0.9992 | LTSM | |||

Wind speed [72] | 0.13–0.27 | 0.14–0.19 | LSTM | 6 h | |||

Wind speed [81] | 0.3509 | 0.5193 | 0.981 | CNN + LSTM | 1–3 h | ||

Wave height and wind speed [37] | 0.844 | CNN + GRU | 1–6 h | ||||

Wave condition and power [54] | 0.49 | LSTM | |||||

Wave height [56] | 0.78 | 0.8638 | 3–24 h | ||||

Wave height [61] | 0.425 | 0.64 | 0.109 | ||||

Wind power [49] | 0.3576 | 0.5058 | - | 0.2173 |

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## Share and Cite

**MDPI and ACS Style**

Gu, C.; Li, H.
Review on Deep Learning Research and Applications in Wind and Wave Energy. *Energies* **2022**, *15*, 1510.
https://doi.org/10.3390/en15041510

**AMA Style**

Gu C, Li H.
Review on Deep Learning Research and Applications in Wind and Wave Energy. *Energies*. 2022; 15(4):1510.
https://doi.org/10.3390/en15041510

**Chicago/Turabian Style**

Gu, Chengcheng, and Hua Li.
2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy" *Energies* 15, no. 4: 1510.
https://doi.org/10.3390/en15041510