  Accurate short-term solar and wind power predictions play an important role
in the planning and operation of power systems. However, the short-term power
prediction of renewable energy has always been considered a complex regression
problem, owing to the fluctuation and intermittence of output powers and the
law of dynamic change with time due to local weather conditions, i.e.
spatio-temporal correlation. To capture the spatio-temporal features
simultaneously, this paper proposes a new graph neural network-based short-term
power forecasting approach, which combines the graph convolutional network
(GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to
learn complex spatial correlations between adjacent renewable energies, and the
LSTM is used to learn dynamic changes of power generation curves. The
simulation results show that the proposed hybrid approach can model the
spatio-temporal correlation of renewable energies, and its performance
outperforms popular baselines on real-world datasets.
