Print Email Facebook Twitter Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning Title Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning Author Rushdi, Mostafa A. (Kyushu University; Future University in Egypt) Rushdi, Ahmad A. (Sandia National Laboratories, California) Dief, Tarek N. (Kyushu University) Halawa, Amr M. (Kyushu University) Yoshida, Shigeo (Kyushu University, Research Institute for Applied Mechanics) Schmehl, R. (TU Delft Wind Energy) Date 2020 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. Subject airborne wind energykite systemkite powertether forcemachine learningneural networkpower predictionPower predictionNeural networkMachine learningKite powerAirborne wind energyKite systemTether force To reference this document use: http://resolver.tudelft.nl/uuid:188aeaf4-5cdb-4cc8-bf0a-26d0d443816f DOI https://doi.org/10.3390/en13092367 ISSN 1996-1073 Source Energies, 13 (9) Part of collection Institutional Repository Document type journal article Rights © 2020 Mostafa A. Rushdi, Ahmad A. Rushdi, Tarek N. Dief, Amr M. Halawa, Shigeo Yoshida, R. Schmehl Files PDF energies_13_02367.pdf 4.57 MB Close viewer /islandora/object/uuid:188aeaf4-5cdb-4cc8-bf0a-26d0d443816f/datastream/OBJ/view