  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.

Machine Learning;Machine Learning