Analysis of wind power curve modeling using multi-model regression

Wind power prediction is vital in renewable energy. Correct forecasts enable utility companies to optimize production and minimize costs. However, due to the intricate nature of wind patterns, making precise predictions is challenging. This article introduces a novel model combining Quantile Regression and Decision Tree Regression for forecasting wind energy. Trained on historical wind speed and output data, the model's efficacy is assessed using metrics like mean absolute error and root mean squared error. The model is evaluated using the SCADA Turkey dataset, a prominent benchmark in wind forecasting. Preliminary results demonstrate the combined model's superior predictive accuracy over traditional regression models, highlighting its potential for enhanced wind energy forecasting.