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LLM Chronos achieves zero/few-shot load forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach for load forecasting in data-scarce environments by leveraging a large language model called Chronos. This LLM framework utilizes its extensive pre-trained knowledge to achieve accurate predictions without requiring extensive fine-tuning on specific datasets. Experiments across five real-world datasets demonstrated that Chronos significantly outperforms nine traditional baseline models in both deterministic and probabilistic forecasting, showing substantial reductions in error metrics. AI

IMPACT Demonstrates LLMs' potential for accurate forecasting in data-limited domains, potentially reducing data acquisition costs and improving efficiency.

RANK_REASON The cluster contains an academic paper detailing a new methodology for load forecasting using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Chronos achieves zero/few-shot load forecasting

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The cluster contains an academic paper detailing a new methodology for load forecasting using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel ·

    Zero and Few Shot Load Forecasting with Large Language Models

    arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce s…

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