Implementation of transformer and LSTM algorithms to forecast the wind power

The main focus of energy generation is to facilitate the increase in demand for energy requirements, which can be non-renewable or renewable energy sources. Since non-renewable energy sources are debilitating at a much faster rate and are harmful to the environment, the requirement for renewable energy has increased. As the second-largest source of renewable energy, wind energy carries a great deal of uncertainty because of its dependence on both the geography and nature of the wind. Wind power forecasting focuses on prediction calculation, which makes it a cheaper and more reliable solution to integrate wind power with the power grid. The recurrent neural network (RNN) outperforms the traditional machine learning techniques in terms of prediction accuracy due to storing the past input in internal memory, which makes it perfectly suited for time series problems. In this proposed work, the Long Short-Term Memory (LSTM) and the transformer algorithm are implemented to forecast wind power production over a medium time frame. An analysis of both prediction algorithms is presented with evaluation matrices for all account features.