Large Language Models Show Promise for Electricity Load Forecasting in Data-Scarce Scenarios
Researchers have demonstrated that the Chronos large language model framework can accurately forecast electricity load in zero- and few-shot settings, outperforming nine established baseline models across five real-world datasets. Traditional deep learning approaches for load forecasting typically require large volumes of domain-specific training data, limiting their use in data-scarce environments. The findings suggest that pre-trained LLMs may offer a flexible, generalizable alternative for energy forecasting tasks where historical data is limited.
A study published on arXiv and accepted in the International Journal of Electrical Power & Energy Systems proposes using the Chronos pre-trained large language model for electricity load forecasting in zero- and few-shot scenarios. The approach leverages Chronos's extensive pre-trained knowledge to make accurate predictions without requiring the model to be fine-tuned on specific load datasets. Tested across five real-world datasets and forecast horizons ranging from 1 to 48 hours, Chronos outperformed nine popular baseline models on both deterministic and probabilistic forecasting tasks. Performance improvements were substantial: reductions in root mean squared error (RMSE) ranged from approximately 7.34% to 84.30%, while continuous ranked probability score (CRPS) and quantile score (QS) improved by 19.63%–60.06% and 22.83%–54.49%, respectively. The authors argue these results position Chronos as a practical solution for energy forecasting in contexts where collecting large training datasets is impractical or costly. The paper spans 24 pages with 5 figures and was updated in June 2026 from its original November 2024 submission.
What's missing
The study does not report computational cost or inference latency of Chronos relative to baseline models, which is relevant for real-world deployment. It is also unclear how the model performs under distribution shifts such as extreme weather events or grid anomalies not represented in the five test datasets. The authors do not discuss the model's interpretability or how practitioners might diagnose forecasting errors, which are common concerns in operational energy systems.
What different sources said
- arXiv cs.LGCenter
Zero and Few Shot Load Forecasting with Large Language Models
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