Abstract

Among the most efficient sources of renewable energy and a potential significant source of electricity is wind energy. Wind energy is perfect for dependable energy systems since it can produce electricity at any time of day. Wind power is a dependable source that may produce renewable energy sources continuously and sustainably. However, there are several drawbacks to wind energy, including as the high initial building costs, the requirement for immovable property, and the challenge of locating ideal locations. In reading, a machine learning (ML)-based scheme is used to forecast the output of wind energy. We suggested a technique for precisely predicting WPs using machine learning techniques. The major goals of this inquiry are to forecast wind production and collect data on daily wind speed control. Only the daily mean wind velocity values were analysed to see if the wind energy estimates provided by the wind speed data were sufficient. The results demonstrated that machine learning techniques could anticipate long-term wind energy values using previous wind data. The results also demonstrate that the machine is designed to find spots that diverge from previously learnt sites so that they can be added to models