Improved Prediction of Wind Speed Using Machine Learning

Abstract

In wind energy systems, wind speed estimation plays an important role. For wind energy systems, accurate forecasting of wind direction is essential, but it is challenging because of its variability. In this paper, wind speed prediction is accomplished using a machine learning-based random forest (RF) method. For the production of wind energy, short-term wind speed prediction is a significant activity. However, it is difficult only to obtain deterministic estimation since wind supplies are erratic and unpredictable. It increases learning that helps to project future values. Average wind speed is a major feature that affects the atmosphere. This paper explains the estimation of wind speed with ML algorithm. It is valuable for assessing the prospects for abnormal climate events and wind energy in the future.