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Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

1
Interdisciplinary Graduate School of Engineering Sciences (IGSES-ESST), Kyushu University, Fukuoka 816-8580, Japan
2
Faculty of Engineering and Technology, Future University in Egypt (FUE), New Cairo 11835, Egypt
3
Sandia National Laboratories, Albuquerque, NM 87123, USA
4
Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
5
Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Energies 2020, 13(9), 2367; https://doi.org/10.3390/en13092367
Received: 31 March 2020 / Revised: 30 April 2020 / Accepted: 1 May 2020 / Published: 9 May 2020
(This article belongs to the Section Wind, Wave and Tidal Energy)
Kites can be used to harvest wind energy at higher altitudes while using only a fraction of the material required for conventional wind turbines. In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models. The system is designed for 7 kW traction power and comprises an inflatable wing with suspended kite control unit that is either tethered to a fixed ground anchor or to a towing vehicle to produce a controlled relative flow environment. A measurement unit was attached to the kite for data acquisition. To predict the generated tether force, we collected input–output samples from a set of well-designed experimental runs to act as our labeled training data in a supervised machine learning setting. We then identified a set of key input parameters which were found to be consistent with our sensitivity analysis using Pearson input–output correlation metrics. Finally, we designed and tested the accuracy of a neural network, among other multivariate regression models. The quality metrics of our models show great promise in accurately predicting the tether force for new input/feature combinations and potentially guide new designs for optimal power generation. View Full-Text
Keywords: airborne wind energy; kite system; kite power; tether force; machine learning; neural network; power prediction airborne wind energy; kite system; kite power; tether force; machine learning; neural network; power prediction
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MDPI and ACS Style

Rushdi, M.A.; Rushdi, A.A.; Dief, T.N.; Halawa, A.M.; Yoshida, S.; Schmehl, R. Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning. Energies 2020, 13, 2367. https://doi.org/10.3390/en13092367

AMA Style

Rushdi MA, Rushdi AA, Dief TN, Halawa AM, Yoshida S, Schmehl R. Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning. Energies. 2020; 13(9):2367. https://doi.org/10.3390/en13092367

Chicago/Turabian Style

Rushdi, Mostafa A.; Rushdi, Ahmad A.; Dief, Tarek N.; Halawa, Amr M.; Yoshida, Shigeo; Schmehl, Roland. 2020. "Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning" Energies 13, no. 9: 2367. https://doi.org/10.3390/en13092367

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