Summary of Automated Deep Learning For Load Forecasting, by Julie Keisler (cristal et al.
Automated Deep Learning for Load Forecasting
by Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 This paper explores novel methodologies for accurate electricity consumption forecasting, particularly deep learning models, to ensure grid performance and stability as renewable energy use increases. Deep Neural Networks (DNNs) struggle with this task due to limited data points and diverse explanatory variables. To overcome these challenges, the authors developed an Automated Deep Learning (AutoDL) framework called EnergyDragon, which automatically selects features, optimizes architecture and hyperparameters, and outperforms state-of-the-art load forecasting methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict how much electricity we’ll use in the future by using a new way of training special computers. It’s hard to do this because lots of things affect how much energy we use, like weather and what day it is. The researchers created a tool that can pick the most important things for the computer to learn from and make better predictions. |
Keywords
» Artificial intelligence » Deep learning
