# Predicting Output Power for Nearshore Wave Energy Harvesting

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. A Summary of Wave Energy Conversion

#### 1.2. Methods for Predicting Power Generation

## 2. Wave Energy Generation

## 3. Methods

#### 3.1. Data Collection

#### 3.2. Data Segmentation and Feature Selection

#### 3.3. Principal Component Analysis

#### 3.4. Machine Learning Algorithms

_{1}, D

_{2}, ..., and D

_{n}. Simultaneously, there were the corresponding average power data, which were also divided into pieces as P

_{1}, P

_{2}, …, and P

_{n}. Using the pairs (D

_{1}, P

_{1}), (D

_{2}, P

_{2}), …, (D

_{m}, P

_{m}) where m < n, we trained ANN, RF and SVM. Then, the trained algorithms can predict (estimate) the output (${\hat{\mathrm{P}}}_{\mathrm{m}+1},\dots ,{\hat{\mathrm{P}}}_{\mathrm{n}}$) with the input (D

_{m+1}, …, D

_{n}). Since we have the actual average power P

_{m+1}, …, P

_{n}, we can compare these actual values with the predicted values to evaluate the ML algorithms.

#### 3.5. Architecture of the Proposed Approach

## 4. Experiments

## 5. Results and Discussion

#### 5.1. Evaluation Metrics

^{2}for the metrics of the regression. These metrics find the difference between actual values and predicted values. The definition of the evaluation metrics is shown in Table 2.

#### 5.2. Wave Tank Experiment

#### 5.2.1. Data Segmentation

#### 5.2.2. Output Power Estimation

^{2}metrics.

^{2}score and the minimum MSE and MAE error values. We selected this model as the prediction model for our proposed approach.

#### 5.3. Actual Wave Energy Harvesting Plant Experiment

#### 5.3.1. Data Segmentation

^{2}value, 0.962, was obtained at the window size of 6000 samples by the ANN regressor.

#### 5.3.2. Output Power Estimation

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Drew, B.; Plummer, A.R.; Sahinkaya, M.N. A review of wave energy converter technology. Proc. IMechE Part A
**2009**, 223, 887–902. [Google Scholar] [CrossRef] - Cruz, J. Ocean Wave Energy: Current Status and Future Prespectives; Springer: Berlin/Heidelberg, Germany, 2007. [Google Scholar]
- Brekken, T.K.; Von Jouanne, A.; Han, H.Y. Ocean wave energy overview and research at oregon state university. In Proceedings of the Power Electronics and Machines in Wind Applications, Lincoln, NE, USA, 24–26 June 2009; Volume 24, pp. 1–7. [Google Scholar]
- Kim, G.; Jeong, W.M.; Lee, K.S.; Jun, K.; Lee, M.E. Offshore and nearshore wave energy assessment around the korean peninsula. Energy
**2011**, 36, 1460–1469. [Google Scholar] [CrossRef] - Korea Energy Agency. Available online: http://www.energy.or.kr/renew_eng/new/standards.aspx (accessed on 2 February 2018).
- Sung, Y.; Kim, J.; Lee, D. Power Converting Apparatus. U.S. Patent 20150275847A1, 1 October 2015. [Google Scholar]
- Margheritini, L.; Vicinanza, D.; Frigaard, P. Ssg wave energy converter: Design, reliability and hydraulic performance of an innovative overtopping device. Renew. Energy
**2009**, 34, 1371–1380. [Google Scholar] [CrossRef] - Czech, B.; Bauer, P. Wave energy converter concepts: Design challenges and classification. IEEE Ind. Electron. Mag.
**2012**, 6, 4–16. [Google Scholar] [CrossRef] - Jang, H.S.; Bae, K.Y.; Park, H.-S.; Sung, D.K. Solar power prediction based on satellite images and support vector machine. IEEE Trans. Sustain. Energy
**2016**, 7, 1255–1263. [Google Scholar] [CrossRef] - Phaiboon, S.; Tanukitwattana, K. Fuzzy model for predicting electric generation from sea wave energy in Thailand. In Proceedings of the Region 10 Conference (TENCON), Singapore, 22–25 November 2016; pp. 2646–2649. [Google Scholar]
- Shi, J.; Lee, W.-J.; Liu, Y.; Yang, Y.; Wang, P. Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl.
**2012**, 48, 1064–1069. [Google Scholar] [CrossRef] - Perera, K.S.; Aung, Z.; Woon, W.L. Machine learning techniques for supporting renewable energy generation and integration: A survey. In Proceedings of the International Workshop on Data Analytics for Renewable Energy Integration, Nancy, France, 19 September 2014; Springer: Cham, Switzerland, 2014; pp. 81–96. [Google Scholar]
- Ingine, Inc. Available online: http://www.ingine.co.kr/en/ (accessed on 2 February 2018).
- Deberneh, H.M.; Kim, I. Wave Power Prediction Based on Regression Models. In Proceedings of the 18th International Symposium on Advanced Intelligent Systems, Daegu, Korea, 11–14 October 2017. [Google Scholar]
- Banos, O.; Galvez, J.-M.; Damas, M.; Pomares, H.; Rojas, I. Window size impact in human activity recognition. Sensors
**2014**, 14, 6474–6499. [Google Scholar] [CrossRef] [PubMed] - Hira, Z.M.; Gillies, D.F. A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinform.
**2015**, 2015. [Google Scholar] [CrossRef] [PubMed] - Juszczak, P.; Tax, D.; Duin, R.P. Feature scaling in support vector data description. In Proceedings of the ASCI; Citeseer: State College, PA, USA, 2002; pp. 95–102. [Google Scholar]
- Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst.
**1987**, 2, 37–52. [Google Scholar] [CrossRef] - Samuel, A.L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev.
**1959**, 3, 210–229. [Google Scholar] [CrossRef] - Chapelle, O.; Vapnik, V.; Bousquet, O.; Mukherjee, S. Choosing multiple parameters for support vector machines. Mach. Learn.
**2002**, 46, 131–159. [Google Scholar] [CrossRef] - Liaw, A.; Wiener, M. Classification and regression by randomforest. R News
**2002**, 2, 18–22. [Google Scholar] - Peres, D.; Iuppa, C.; Cavallaro, L.; Cancelliere, A.; Foti, E. Significant wave height record extension by neural networks and reanalysis wind data. Ocean Model.
**2015**, 94, 128–140. [Google Scholar] [CrossRef] - Raschka, S. Python Machine Learning; Packt Publishing Ltd.: Birmingham, UK, 2015. [Google Scholar]
- Hsu, C.-W.; Chang, C.-C.; Lin, C.-J. A Practical Guide to Support Vector Classification; National Taiwan University: Taipei City, Taiwan, 2003. [Google Scholar]
- Scikit Learn. Available online: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html (accessed on 2 February 2018).
- Trivedi, S.; Pardos, Z.A.; Heffernan, N.T. Clustering students to generate an ensemble to improve standard test score predictions. In Proceedings of the International Conference on Artificial Intelligence in Education, Auckland, New Zealand, 28 June–1 July 2011; Springer: Berlin/Heidelberg, Germany, 2011; pp. 377–384. [Google Scholar]

**Figure 1.**Schematic diagram of nearshore WEC configuration [13].

**Figure 2.**Working principle of the nearshore WEC [13] (

**a**) when the buoy rises and (

**b**) when the buoy descends.

**Figure 7.**INGINE wave energy harvesting station. (

**a**) Scene from the experimental site located at Bukchon in Jejudo. (

**b**) Simulated arrangement of the connecting ropes of the buoy.

Experimental Cases | Actual Values | Scaled Values | Scaled Average Power (W) | ||
---|---|---|---|---|---|

Height | Period | Height | Period | ||

Case 1 | 4 m | 12 s | 20 cm | 2.68 s | 9.36 |

Case 2 | 3 m | 10 s | 15 cm | 2.24 s | 5.82 |

Case 3 | 2.5 m | 10 s | 12.5 cm | 2.24 s | 3.22 |

Metric | Definition |
---|---|

$\mathrm{MAE}$ | $=\frac{1}{{\mathrm{n}}_{\mathrm{s}}}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=0}^{{\mathrm{n}}_{\mathrm{s}}-1}}\left|{\hat{\mathrm{y}}}_{\mathrm{i}}-{\mathrm{y}}_{\mathrm{i}}\right|$ |

$\mathrm{MSE}$ | $=\frac{1}{{\mathrm{n}}_{\mathrm{s}}}{\displaystyle {\displaystyle \sum}_{\mathrm{i}=0}^{{\mathrm{n}}_{\mathrm{s}}-1}}{\left({\hat{\mathrm{y}}}_{\mathrm{i}}-{\mathrm{y}}_{\mathrm{i}}\right)}^{2}$ |

${\mathrm{R}}^{2}$ | $=1-\frac{{{\displaystyle \sum}}_{\mathrm{i}=0}^{{\mathrm{n}}_{\mathrm{s}}-1}{\left({\hat{\mathrm{y}}}_{\mathrm{i}}-{\mathrm{y}}_{\mathrm{i}}\right)}^{2}}{{{\displaystyle \sum}}_{\mathrm{i}=0}^{{\mathrm{n}}_{\mathrm{s}}-1}{\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{y}}\right)}^{2}}$ |

Window Size | 12 | 18 | 24 | 26 | 32 | 36 | 72 |
---|---|---|---|---|---|---|---|

Number of training | 657 | 438 | 329 | 303 | 246 | 220 | 111 |

Number of testing | 282 | 189 | 142 | 131 | 106 | 95 | 48 |

Window Size | 600 | 1200 | 1800 | 2400 | 3000 | 3600 | 4200 | 4800 | 5400 | 6000 |
---|---|---|---|---|---|---|---|---|---|---|

Number of training | 3625 | 1813 | 1208 | 906 | 725 | 604 | 518 | 453 | 403 | 362 |

Number of testing | 1554 | 777 | 519 | 389 | 311 | 260 | 222 | 195 | 173 | 156 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Deberneh, H.M.; Kim, I. Predicting Output Power for Nearshore Wave Energy Harvesting. *Appl. Sci.* **2018**, *8*, 566.
https://doi.org/10.3390/app8040566

**AMA Style**

Deberneh HM, Kim I. Predicting Output Power for Nearshore Wave Energy Harvesting. *Applied Sciences*. 2018; 8(4):566.
https://doi.org/10.3390/app8040566

**Chicago/Turabian Style**

Deberneh, Henock Mamo, and Intaek Kim. 2018. "Predicting Output Power for Nearshore Wave Energy Harvesting" *Applied Sciences* 8, no. 4: 566.
https://doi.org/10.3390/app8040566