Wind Speed Forecasting using Long Short Term Memory Networks

Globally, wind power industry is growing very fast, but intermittency of the wind is causing difficulty in integrating wind power to the grid due to issues such as power scheduling, system control and dispatch, voltage regulation and frequency response reserve, energy imbalance etc. So, accurate wind speed prediction is recommended for scheduling the wind power to maintain stability in the power system. In this paper, long short term memory network (LSTM) is proposed for predicting wind speed one hour ahead by clustering the time series data into windy and non-windy months. LSTM model is applied to forecast wind speed for four different sites and results are presented.