Summary of An Efficient and Explainable Transformer-based Few-shot Learning For Modeling Electricity Consumption Profiles Across Thousands Of Domains, by Weijie Xia et al.
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of Domains
by Weijie Xia, Gao Peng, Chenguang Wang, Peter Palensky, Eric Pauwels, Pedro P. Vergara
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 proposes a novel few-shot learning (FSL) method for modeling Electricity Consumption Profiles (ECPs), addressing challenges in data-scarce scenarios. The traditional FSL methods are unsuitable for ECP modeling due to the assumption of sufficient data and cumbersome knowledge transfer mechanisms. This new approach exploits Transformers and Gaussian Mixture Models (GMMs) for lightweight and interpretable ECP modeling. Results show that this method can accurately restore complex ECP distributions with minimal data, outperforming state-of-the-art time series modeling methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to predict electricity consumption patterns even when there’s not much data available. This is important because it helps plan and operate power grids more efficiently. The researchers developed a new way of using machine learning models that are both fast and easy to understand. They tested their method on real-world data and showed that it works better than other approaches. |
Keywords
» Artificial intelligence » Few shot » Machine learning » Time series
