Summary of Operational Wind Speed Forecasts For Chile’s Electric Power Sector Using a Hybrid Ml Model, by Dhruv Suri et al.
Operational Wind Speed Forecasts for Chile’s Electric Power Sector Using a Hybrid ML Model
by Dhruv Suri, Praneet Dutta, Flora Xue, Ines Azevedo, Ravi Jain
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel hybrid machine learning (ML) approach to improve forecasting accuracy for renewable energy generation. Specifically, it introduces a method that combines two custom ML models, TiDE and GraphCast, to predict wind speed up to 10 days in advance. This approach outperforms traditional operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts. The proposed methodology has the potential to directly reduce the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve renewable energy forecasting in Chile by developing a hybrid machine learning approach that combines two custom models: TiDE and GraphCast. These models are specifically designed for short-term and medium-term forecasts. The results show that the proposed methodology outperforms traditional methods, which can help reduce the impact of wind generation on thermal power plants. |
Keywords
* Artificial intelligence * Machine learning
