Summary of Few-shot Load Forecasting Under Data Scarcity in Smart Grids: a Meta-learning Approach, by Georgios Tsoumplekas et al.
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach
by Georgios Tsoumplekas, Christos L. Athanasiadis, Dimitrios I. Doukas, Antonios Chrysopoulos, Pericles A. Mitkas
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel meta-learning algorithm for short-term load forecasting in smart grids is proposed, which rapidly adapts and generalizes within any unknown load time series using minimal training samples. By learning an optimal set of initial parameters for a base-level learner recurrent neural network, the model outperforms transfer learning and task-specific machine learning methods by 12.5% on a real-world consumer dataset. The approach is evaluated using mean average log percentage error (MALPE), which alleviates bias introduced by the commonly used MAPE metric. Robustness under different hyperparameters and time series lengths is demonstrated, with the proposed model consistently outperforming all others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict how much energy people will use in their homes or businesses has been developed. This method can learn from very little information and make good predictions even when it doesn’t have a lot of data. It’s better than other methods that need more data to work well. The researchers tested this approach using real-world data from customers and found that it worked really well, predicting the future energy use with high accuracy. |
Keywords
» Artificial intelligence » Machine learning » Meta learning » Neural network » Time series » Transfer learning
