Enhancing wind power forecasting from meteorological parameters using machine learning models.

Short-term wind power forecasting is crucial for power system stability, dispatching, and cost control. Wind energy has the potential to be a viable source of renewable energy. Wind power generation forecasting is vital for resolving the supply and demand challenges of the smart grid. Moreover, one of the most problematic aspects of wind power is its high fluctuation and intermittent nature, which makes forecasting difficult. The goal of this research is to create machine learning models that can properly estimate wind power production. Significantly, the major contributions of this work are highlighted in the following significant elements. First, a data analysis framework for visualizing the gathered dataset from the SCADA system is presented. Second, for forecasting wind power time-series dataset values, we examine the predicting performance of various machine learning models using various statistical indices. The experimental findings demonstrate that with a minor reconstruction error, the proposed forecast approaches can minimize the complexity of the forecasting. Furthermore, in terms of forecast accuracy, a gradient boosting regression model outperforms other benchmark models. According to the analysis, our methodology might be applied in real-world circumstances to assist the management group in regulating the power provided by wind turbines. [ABSTRACT FROM AUTHOR]