Wind Speed Forecasting Using Long Short-Term Memory (LSTM) Networks
Researcher: Sindisiwe Zulu, University of the Witwatersrand, Johannesburg
Supervisor: Mr Rendani Mbuvha, University of the Witwatersrand, Johannesburg
Wind energy is seen as the next promising renewable energy to be used for future power generation. The stochastic wind behavior has resulted in the development of improved wind forecasting techniques. New techniques are required for wind speed forecasting. This research investigates Long Short Term Memory (LSTM) networks for 1 hour to 3 hours ahead forecasting of wind speed. In comparison with LSTM, the Multilayer Perceptron (MLP) model tests the LSTM model’s efficiency. From the results it is shown that the LSTM model outperformed the MLP model however, the differences in performance are not statistically significant. Best results were obtained from 1 hour ahead forecasting.