  Present energy demand and modernization are leading to greater fossil fuel
consumption, which has increased environmental pollution and led to climate
change. Hence to decrease dependency on conventional energy sources, renewable
energy sources are considered. Wind energy is a long-term renewable energy
resource but its intermittent nature makes it difficult in harnessing it. Since
wind speed prediction is vital there are different methodologies for wind speed
estimation available in the literature. In this work, a new hybrid model is
proposed by combining auto-regressive integrated moving average (ARIMA), Kalman
filter and long short-term memory (LSTM) for estimating wind speed which works
more accurately than the existing methods proposed in the literature. From
simulations, it is observed that the proposed method works with better accuracy
when compared to the existing methods.
