  Wind speed is a powerful source of renewable energy, which can be used as an
alternative to the non-renewable resources for production of electricity.
Renewable sources are clean, infinite and do not impact the environment
negatively during production of electrical energy. However, while eliciting
electrical energy from renewable resources viz. solar irradiance, wind speed,
hydro should require special planning failing which may result in huge loss of
labour and money for setting up the system. In this paper, we discuss four deep
recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU,
Stacked Stateful LSTM and Statcked Stateful GRU which will be used to predict
wind speed on a short-term basis for the airport sites beside two campuses of
Mississippi State University. The paper does a comprehensive analysis of the
performance of the models used describing their architectures and how
efficiently they elicit the results with the help of RMSE values. A detailed
description of the time and space complexities of the above models has also
been discussed.
