A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting
This work addresses wind speed prediction for wind power producers and grid operators, but it is incremental as it builds on existing LSTM methods with multi-variable inputs.
The paper tackled short-term wind speed forecasting under uncertainties by proposing a multi-variable stacked LSTMs model (MSLSTM) that uses historical meteorological variables, and it achieved competitive performance on real data from West Texas, USA.
Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance.