  Power dispatch is essential for providing stable, cost-effective, and
eco-friendly electricity to society. However, traditional methods falter as
power systems grow in scale and complexity, struggling with multitasking, swift
problem-solving, and human-machine collaboration. This paper introduces GAIA,
the pioneering Large Language Model (LLM) tailored for power dispatch tasks. We
have developed a novel dataset construction technique that harnesses a range of
data sources to fine-tune GAIA for optimal performance in this domain. This
approach streamlines LLM training, allowing for the seamless integration of
multidimensional data in power system management. Additionally, we have crafted
specialized prompt strategies to boost GAIA's input-output efficiency in
dispatch scenarios. When evaluated on the ElecBench benchmark, GAIA surpasses
the baseline model LLaMA2 on multiple metrics. In practical applications, GAIA
has demonstrated its ability to enhance decision-making processes, improve
operational efficiency, and facilitate better human-machine interactions in
power dispatch operations. This paper expands the application of LLMs to power
dispatch and validates their practical utility, paving the way for future
innovations in this field.
