Tether Force Estimation Airborne Kite using Machine Learning Methods

Airborne Wind Energy (AWE) is looking very promising for harnessing high- altitute winds and aiding in the transition from fossil fuels to sustainable en- ergy. The ground-based kite system in plays a crucial role in autonomously estimating tether force, which depends on various factors such as wind speed, the kite’s orientation relative to the wind vector in its figure-eight trajectory and Latitute as well as Longitude. To predict tether force, we have em- ployed testing of four regression machine learning models which have shown merit in similar fields. The machine learning models which were tested upon were: Linear Regression Support Vector Machine Regression Random Forest Regression XGBoost Regressor Gradient Boosting After getting the metrics which were Mean Absolute Error(MAE), Root Mean Square Error(RMSE) and R Error, we concluded that XGBoost Re- gressor gave us the best metrics in all three categories.