Artificial Intelligence for Renewable Energy Systems

6Deep Learning Algorithms for Wind Forecasting: An Overview

M. Lydia¹* and G. Edwin Prem Kumar²

¹Dept. of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India

²Dept. of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India

Abstract

India, having the fourth largest installed capacity of wind power, is poised to grow in leaps and bounds in renewable energy utilization. The stochastic nature of wind has been a constant challenge in integration of wind power to the grid. According to the National Institute of Wind Energy (NIWE), the estimated wind potential of India at 120 m above ground level is around 695 GW. In order to effectively tap this power and to enhance wind power penetration in the grid, it is imperative that efficient wind speed and wind power forecasting models are in place. Forecasting of wind power aids in effective grid operations, planning of economic dispatch, estimation of candidate sites for wind farms and in scheduling operation and maintenance of wind farms. Deep learning models for wind forecasting have recently challenged the conventionally used forecasting models in terms of their accuracy, robust nature, and ability to handle huge volumes of data at a much lower computational cost. An exhaustive review of all the deep learning models used for wind speed/power forecasting is reviewed in this chapter. The research challenges faced and future research directions are also presented. ...