Machine Learning-Based Forecasting Models for Wind Turbine Power Output

Abstract

Wind power is unpredictable due to chaotic nature of wind speed. This makes it important to accurately predict how much power a wind farm will generate.Predicting wind power helps balance the electricity supply and demand, which is very important for a stable power grid. These predictions also help other conventional power plants to mitigate the load demand. It also helps us trade electricity more efficiently with profitable cost. In this paper, we look at different methodologies to predict wind power. It starts by discussing how we use various algorithms to make predictions most accurately with less error. Using statistical and machine learning approaches we can able to get the solution in most precise way. Recent methodologies are the hybrid network models which have good prediction accuracy with less error. These all algorithms work on past data in large quantities which we are collecting from different wind farms. The paper gives a comparative analysis of various literatures and described which methodology is the best among all depending on various seasonal data and ultra short-term prediction.