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GAIA -- A Large Language Model for Advanced Power Dispatch

Yuheng Cheng, Huan Zhao, Xiyuan Zhou, Junhua Zhao, Yuji Cao, Chao Yang

TL;DR

GAIA introduces a domain-specific Large Language Model tailored for power dispatch by fusing simulation-derived numerical data with textual knowledge and employing specialized prompts. The authors present a multi-stage data pipeline, including simulation data generation, text data processing, and Deep Knowledge-Guided prompting, trained on a LLaMA2 backbone using LoRA-based fine-tuning across 7b, 13b, and 70b configurations. Evaluated on the ElecBench benchmark, GAIA-70b outperforms LLaMA2 and GPT-3.5 across multiple power-dispatch metrics, with safety and reliability often surpassing peer models and approaching GPT-4-level performance in some tasks. The work demonstrates the practical utility of domain-adapted LLMs for real-time decision support, improved human–machine collaboration, and more efficient power system operation, while acknowledging remaining gaps in language robustness and extreme-condition handling.

Abstract

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.

GAIA -- A Large Language Model for Advanced Power Dispatch

TL;DR

GAIA introduces a domain-specific Large Language Model tailored for power dispatch by fusing simulation-derived numerical data with textual knowledge and employing specialized prompts. The authors present a multi-stage data pipeline, including simulation data generation, text data processing, and Deep Knowledge-Guided prompting, trained on a LLaMA2 backbone using LoRA-based fine-tuning across 7b, 13b, and 70b configurations. Evaluated on the ElecBench benchmark, GAIA-70b outperforms LLaMA2 and GPT-3.5 across multiple power-dispatch metrics, with safety and reliability often surpassing peer models and approaching GPT-4-level performance in some tasks. The work demonstrates the practical utility of domain-adapted LLMs for real-time decision support, improved human–machine collaboration, and more efficient power system operation, while acknowledging remaining gaps in language robustness and extreme-condition handling.

Abstract

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.
Paper Structure (35 sections, 11 figures, 4 tables)

This paper contains 35 sections, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Domain-specific Large Language Models
  3. Specific Large Language Models
  4. Dataset Generation Methods
  5. Baseline LLM
  6. Parameter Efficient Fine-tuning Methods
  7. Methodology
  8. Framework
  9. Data Collection and Preprocessing
  10. Simulation Data Processing
  11. Dispatch Scenario Task Division
  12. Simulation Data Generation
  13. Q&A Instruction Generation
  14. Text Data Processing
  15. Prompt Designing
  16. ...and 20 more sections

Figures (11)

  • Figure 1: Overall Framework of GAIA
  • Figure 2: Simulation Data Processing Flow
  • Figure 3: Text Data Processing Flow
  • Figure 4: Prompt Designing
  • Figure 5: Training Dataset Composition
  • ...and 6 more figures