Summary of Improve Load Forecasting in Energy Communities Through Transfer Learning Using Open-access Synthetic Profiles, by Lukas Moosbrugger et al.
Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles
by Lukas Moosbrugger, Valentin Seiler, Gerhard Huber, Peter Kepplinger
First submitted to arxiv on: 11 Jul 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 This paper presents a novel approach to improving load profile forecasts in energy communities with limited historical data availability. By pre-training models using open-access synthetic load profiles and transfer learning techniques, the authors demonstrate significant improvements in training stability and prediction error. Specifically, the proposed method reduced the mean squared error (MSE) from 0.34 to 0.13 in a test case involving 74 households. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to accurately predict how much energy people will use in the future. This is important because it can help save money and make the power grid more efficient. The authors found that by using fake data and teaching models with this information, they could get better predictions even when there wasn’t much real data available. This could be helpful for communities just starting to generate their own energy. |
Keywords
* Artificial intelligence * Mse * Transfer learning
