A Hybrid Approach using ARIMA, Kalman Filter and LSTM for Accurate Wind Speed Forecasting
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.