Short–Term Forecasting of Wind Energy Using Linear Regression Approach

Wind generation forecasts indicate a dynamic role in the production of renewable energy. The unpredictability of wind power makes forecasting it a particularly difficult task. For this reason, accurate forecasting methods are required. This study demonstrates the use of a machine learning technique based on linear regression to identify the underlying relationships among wind data and to predict precise wind generation. Regression analysis is done on wind data to remove any invisible traits and identify important details. It has also been utilized to foster deeper connections between the values. The National Renewable Energy Laboratory's (NREL) collection of 3 distinct datasets (hourly, monthly & yearly) is used to implement the propose strategy to modify the power database. The anticipated results suggest that the inquiry may accurately predict wind power using a spread range from hours to years. By more correctly forecasting wind speed and output power, power system scheduling may be done to quickly change the scheduling plans, minimizing the influence of wind power on the electric power grid. Additionally, it lowers operational expenses for the power system and raises the power limit for wind power penetration.