  This project presents an extension to the GraphCast model, a state-of-the-art
graph neural network (GNN) for global weather forecasting, by integrating solar
energy production forecasting capabilities. The proposed approach leverages the
weather forecasts generated by GraphCast and trains a neural network model to
predict the ratio of actual solar output to potential solar output based on
various weather conditions. The model architecture consists of an input layer
corresponding to weather features (temperature, humidity, dew point, wind
speed, rain, barometric pressure, and altitude), two hidden layers with ReLU
activations, and an output layer predicting solar radiation. The model is
trained using a mean absolute error loss function and Adam optimizer. The
results demonstrate the model's effectiveness in accurately predicting solar
radiation, with its convergence behavior, decreasing training loss, and
accurate prediction of solar radiation patterns suggesting successful learning
of the underlying relationships between weather conditions and solar radiation.
The integration of solar energy production forecasting with GraphCast offers
valuable insights for the renewable energy sector, enabling better planning and
decision-making based on expected solar energy production. Future work could
explore further model refinements, incorporation of additional weather
variables, and extension to other renewable energy sources.
