Gated Recurrent Unit (GRU) Based Short Term Forecasting for Wind Energy Estimation

Penetration of renewable energy generation is increasing rapidly due to the increasing demand of the grid. From the power system operation and economy point of view, wind power integration plays a vital role in the emerging grid. However, due to the presence of uncertainty in the wind speed, its prediction is also a significant factor in the integration of wind power. The accuracy of wind speed prediction can help power system operator to overcome the risk of unreliability power supply. Although several data prepossessing and prediction approaches have been described in the literature, these approaches faceing the problem of high accuracy. To solve this problem, this paper presents the various machine learning algorithms for accurate forecasting of wind speed and the consequent estimation of generated energy. All algorithms are applied to hourly wind speed data for the region of Jodhpur, Rajasthan (India). The numerical results indicating that the GRU performance outperformed them