Power Prediction of Airborne Wind Energy Systems Using Multivariate Machine Learning
Published in MDPI, Energies, 2020
Recommended citation: Rushdi, M. A., Rushdi, A. A., Dief, T. N., Halawa, A. M., Yoshida, S., & Schmehl, R. (2020). Power prediction of airborne wind energy systems using multivariate machine learning. Energies, 13(9), 2367. /files/papers/AWE_ML.pdf
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.
Recommended citation: Rushdi, M. A., Rushdi, A. A., Dief, T. N., Halawa, A. M., Yoshida, S., & Schmehl, R. (2020). Power prediction of airborne wind energy systems using multivariate machine learning. Energies, 13(9), 2367.