Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning

Mostafa A. Rushdi, Ahmad A. Rushdi, Tarek N. Dief, Amr M. Halawa, Shigeo Yoshida and Roland Schmehl
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Mostafa A. Rushdi: Interdisciplinary Graduate School of Engineering Sciences (IGSES-ESST), Kyushu University, Fukuoka 816-8580, Japan
Ahmad A. Rushdi: Sandia National Laboratories, Albuquerque, NM 87123, USA
Tarek N. Dief: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Amr M. Halawa: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Shigeo Yoshida: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Roland Schmehl: Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands

Energies, 2020, vol. 13, issue 9, 1-23

Abstract: 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.

Keywords: airborne wind energy; kite system; kite power; tether force; machine learning; neural network; power prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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