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.

该研究扩展了 GraphCast 模型，将太阳能发电预测能力整合进去，并成功地预测了天气条件与太阳辐射之间的关系，为可再生能源部门提供宝贵的见解。

Solarcast-ML: 太阳能发电的每节点图传播扩展

Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production

Since the weather is chaotic, forecasts aim to predict the distribution of
future states rather than make a single prediction. Recently, multiple data
driven weather models have emerged claiming breakthroughs in skill. However,
these have mostly been benchmarked using deterministic skill scores, and little
is known about their probabilistic skill. Unfortunately, it is hard to fairly
compare AI weather models in a probabilistic sense, since variations in choice
of ensemble initialization, definition of state, and noise injection
methodology become confounding. Moreover, even obtaining ensemble forecast
baselines is a substantial engineering challenge given the data volumes
involved. We sidestep both problems by applying a decades-old idea -- lagged
ensembles -- whereby an ensemble can be constructed from a moderately-sized
library of deterministic forecasts. This allows the first parameter-free
intercomparison of leading AI weather models' probabilistic skill against an
operational baseline. The results reveal that two leading AI weather models,
i.e. GraphCast and Pangu, are tied on the probabilistic CRPS metric even though
the former outperforms the latter in deterministic scoring. We also reveal how
multiple time-step loss functions, which many data-driven weather models have
employed, are counter-productive: they improve deterministic metrics at the
cost of increased dissipation, deteriorating probabilistic skill. This is
confirmed through ablations applied to a spherical Fourier Neural Operator
(SFNO) approach to AI weather forecasting. Separate SFNO ablations modulating
effective resolution reveal it has a useful effect on ensemble dispersion
relevant to achieving good ensemble calibration. We hope these and forthcoming
insights from lagged ensembles can help guide the development of AI weather
forecasts and have thus shared the diagnostic code.

天气模型的概率技能与延迟集合相比较，发现 GraphCast 和 Pangu 是领先的 AI 天气模型，在确定性评分方面前者优于后者，但在概率 CRPS 指标上两者得分相同。