Short term forecasting based on hourly wind speed data using deep learning algorithms

Application of Internet of Things (IoT) in smart grid is evident in current trends. Smart grid management have greater impact on market economics, security, and distribution of energy. Smart grid is an integration of several components like, wind, solar, cyber-security etc. One of major concern in smart grid is optimal control of wind generation and accurate prediction of wind speed. This paper aims to predict the wind speed with meteorological time series data as input variable using deep learning topology for one-year wind speed data. The dynamic recurrent type network (RNN) integrates and processed with the Extreme Learning-Machine (ELM), nonlinear autoregressive network with exogenous inputs (NARX), and Long short-term memory (LSTM) model. Three models having the same Network’s architecture, intermediate layer in architecture have 19 neurons and an activation function. Feature selection method is used for feature extraction from wind data (have four features as wind speed, pressure, humidity, air temperature) and applied to models. Comparative analysis of different models are assessed by performance matrices such as MAPE, MAE, and RMSE.