Zero and Few Shot Load Forecasting with Large Language Models
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.