Machine Learning Method for Forecasting Wind Power Using Continuous Wind Speed Data

Abstract

Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based  Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.