Publication Type : Conference Proceedings
Publisher : 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)
Source : 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), IEEE, Kannur,Kerala, India (2019)
Url : https://ieeexplore.ieee.org/document/8993279
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : 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.
Cite this Research Publication : P. P.P, Vanitha, V., and Resmi, R., “Wind Speed Forecasting using Long Short Term Memory Networks”, in 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur,Kerala, India, 2019.