Summary of Solarcast-ml: Per Node Graphcast Extension For Solar Energy Production, by Cale Colony et al.
Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production
by Cale Colony, Razan Andigani
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach integrates solar energy production forecasting capabilities into the GraphCast model, a state-of-the-art graph neural network for global weather forecasting. The model predicts the ratio of actual solar output to potential solar output based on various weather conditions. It consists of an input layer, 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 accurate prediction of solar radiation patterns. This integration offers valuable insights for the renewable energy sector, enabling better planning and decision-making based on expected solar energy production. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GraphCast model is used to predict the weather, and now it can also tell us how much solar energy we will get from the sun. The new model uses information like temperature, humidity, and wind speed to figure out how much solar power we can expect. It’s good at predicting this, which helps people make better decisions about using renewable energy. |
Keywords
» Artificial intelligence » Graph neural network » Loss function » Relu » Temperature
