Publication

Abstract : Generally, wind speed prediction plays a vital role in generation of wind power. Lately, wind power generation has developed quickly, and the exactness expectation of wind power generation is vital due to the effect on the security of power frameworks. Notwithstanding, the varieties of wind speeds is incredibly high, making the prediction of wind power generation very troublesome. Initially, correlation analysis of different input parameters is considered and the parameters with the higher correlation are scrutinized and then they are considered to be the ultimate inputs for prediction of wind speed. In this paper, errors are compared between two noted deep learning algorithms namely, Long Short -Term Memory (LSTM) and Gated Recurrent Unit (GRU) and the final conclusion has shown that GRU gives better results compared to LSTM in predicting the wind speed. However, Both LSTM and GRU have their own set of pros and cons.