Abstract
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
2 Error, we concluded that XGBoost Re- gressor gave us
the best metrics in all three categories.