An on-premises end-to-end automated forecasting multi-agent system for the energy domain

Time-series forecasting in the energy sector is a labor-intensive process requiring expertise in multiple areas, such as data preprocessing, feature engineering, and neural network optimization. Although large language models offer automation potential, existing large-language-model-based multi-agent systems lack specialized structures for forecasting workflows, balancing computational cost and tool capability remains challenging. To address these issues, we propose an Energy-domain End-to-end Automated Forecasting Multi-Agent System, an on-premises end-to-end forecasting framework built upon a novel Department-Collaborative Multi-agent System Structure, specifically fine-tuned for energy forecasting applications. Within the Energy-domain End-to-end Automated Forecasting Multi-Agent System dominated by Department-Collaborative Multi-agent System Structure, two core and innovative modules are introduced: (i) Structured Behavioral Knowledge Distillation, which enables small-parameter large language models to operate feature engineering tools, facilitating lightweight and efficient on-premises deployment; and (ii) the Alignment-to-Selection Framework, which automates neural network selection by integrating architectural knowledge with historical performance data. Extensive experimental results demonstrate that the novel Energy-domain End-to-end Automated Forecasting Multi-Agent System significantly reduces manual effort, while maintaining high forecasting accuracy across diverse datasets.