Regression Method-Based Prediction of Wind Power Generation

Wind power prediction is essential in tackling the challenges of balancing supply and demand in any electrical system, given the uncertainty surrounding the output of wind farms. To cut emissions and encourage sustainable growth, certain countries have recently shifted to renewable energy sources, particularly wind and solar energy. Given that renewable energy sources are incorporated into already-existing grids and blended with conventional sources, determining the amount of electricity that will be produced is crucial to minimising the wind farm's operational costs and guaranteeing the power grid's safety. Accurate wind power projections help to reduce the need for extra reserve and balancing power to incorporate wind power. With the help of machine learning (ML) techniques and historical wind power generation data, this system provides a prediction model to address a real-world problem in the field of renewable energy. It accurately predicts the quantity of wind energy production each hour during the following 24 hours. Since meteorological conditions are unpredictable, wind power generated by wind turbines cannot be planned. So, in order to conduct activities such as energy trading, load tracking, pre-load sharing, turbine control, and others, wind power forecasts are required. Data mining is a commonly used approach for analysing enormous amounts of data in all fields. Researchers employ a variety of data mining and machine learning techniques to analyse massive, complex environmental data and help experts forecast the generation of wind power. The proposed method entails developing a machine learning model that can forecast power generation. When evaluating alternative regression algorithms, the decision tree is the most effective model for outcome prediction.