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In this project, I have employed various regression techniques to estimate the Power curve of an on-shore Wind turbine. Nonlinear trees based ensemble regression methods perform best as true power curve is nonlinear. I have implemented and optimized XGBoost using GridSearchCV that yields lowest Test RMSE-6.404.
Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To predict the date when equipment will completely fail (RUL), XGBoost is used and achieved RMSE error is 0.033964 days, which is highly accurate.
Wind Turbine Placement Optimization in Switzerland is a project dedicated to achieving sustainable energy goals through intelligent wind turbine placement, optimizing geographical parameters with Google OR tools. Created as a course project in Sustainable Management and Technology