Summary of Zero-shot Load Forecasting with Large Language Models, by Wenlong Liao et al.
Zero-Shot Load Forecasting with Large Language Models
by Wenlong Liao, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Porté-Agel
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 The proposed Chronos model uses a pre-trained language model framework to enable zero-shot load forecasting in data-scarce scenarios. By leveraging its extensive knowledge, the Chronos model achieves accurate load forecasting without requiring extensive data-specific training. Simulation results across five real-world datasets demonstrate that the Chronos model outperforms nine popular baseline models for both deterministic and probabilistic load forecasting with various forecast horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Load forecasting has become a crucial task in modern energy management. This paper proposes a novel approach to load forecasting using advanced language models, which can accurately predict load patterns even without extensive training data. The Chronos model uses pre-trained knowledge to make predictions, making it an effective solution for data-scarce scenarios. |
Keywords
* Artificial intelligence * Language model * Zero shot
