Optimizing Airborne Wind Energy Systems for Sustainable Water Pumping: A Machine Learning Approach

This study presents a comprehensive analysis of integrating machine learning (ML) algorithms into an Airborne Wind Energy System (AWES) Pumping Kit (PK) to optimize its performance in harnessing wind energy for water pumping applications. The focus is on predicting wind speeds, a crucial factor for maximizing energy generation and pumping efficiency. Historical wind speed data is collected and preprocessed, followed by the training of a Random Forest Regressor model. The model is evaluated using Root Mean Squared Error (RMSE) on both training and testing datasets. Results show that the ML-based approach effectively predicts wind speeds, enabling the AWES PK to adjust its operation in real-time for optimal performance. This analysis demonstrates the feasibility and benefits of integrating ML techniques into renewable energy systems, paving the way for more efficient and sustainable solutions in water pumping and beyond.

Keywords:

Airborne Wind Energy Systems , Sustainable Water Pumping , Machine Learning Optimization , Renewable Energy Technology , Environmental Sustainability.