Summary of Automated Spatio-temporal Weather Modeling For Load Forecasting, by Julie Keisler (cristal et al.
Automated Spatio-Temporal Weather Modeling for Load Forecasting
by Julie Keisler, Margaux Bregere
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 abstract discusses the challenge of storing electricity and maintaining a balance between generation and load. To achieve this, accurate forecasting of electricity load and renewable production is crucial, which relies heavily on meteorological variables like temperature, wind, and sunshine. The dependencies are complex and difficult to model due to spatial and temporal variations. The authors propose using deep neural networks to improve spatio-temporal weather modeling for load forecasting, comparing their methodology with the state-of-the-art on French national load. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Electricity is hard to store, so we need to keep generation and consumption in balance at all times. To do this, we need to predict how much electricity will be used and when renewable energy sources like wind and solar will produce power. The weather plays a big role in both of these predictions, but it’s tricky to model because different parts of the country have different climates and temperatures can affect how much energy is used hours later. Researchers are using special kinds of computer networks called deep neural networks to try and improve their weather modeling for predicting electricity use. |
Keywords
* Artificial intelligence * Temperature
