Abstract

Wind power prediction plays a key role in dealing with thechallenges of balancing supply and demand in any electrical system, given the uncertainty associated with wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power generated by wind turbines has a non-schedulable nature due to the stochastic nature of meteorological conditions. Hence, wind power predictions are required for turbine control, load tracking, pre-load sharing, power system management, and energy trading. Data mining is a commonly used technique for processing enormous data in all domains. Researchers apply several data mining and machine learning techniques to analyze huge complex environmental data, helping professionals to predict Wind power generation. The proposed method is to build a machine-learning model capable of predicting power generation. Different algorithms are compared and the best model is used for predicting the outcome.