Large Language Model-Empowered Interactive Load Forecasting

TL;DR

This paper introduces an LLM-based multi-agent framework that enhances load forecasting by enabling human-operator interaction, improving accuracy, and making advanced models more accessible to non-experts in power systems.

Contribution

It presents a novel multi-agent collaboration framework leveraging large language models to facilitate human-model interaction in load forecasting.

Findings

Interactive framework improves forecasting accuracy with user insights.

The approach reduces technical barriers for non-expert users.

Cost analysis confirms practical deployment feasibility.

Abstract

The growing complexity of power systems has made accurate load forecasting more important than ever. An increasing number of advanced load forecasting methods have been developed. However, the static design of current methods offers no mechanism for human-model interaction. As the primary users of forecasting models, system operators often find it difficult to understand and apply these advanced models, which typically requires expertise in artificial intelligence (AI). This also prevents them from incorporating their experience and real-world contextual understanding into the forecasting process. Recent breakthroughs in large language models (LLMs) offer a new opportunity to address this issue. By leveraging their natural language understanding and reasoning capabilities, we propose an LLM-based multi-agent collaboration framework to bridge the gap between human operators and forecasting models. A set of specialized agents is designed to perform different tasks in the forecasting workflow and collaborate via a dedicated communication mechanism. This framework embeds interactive mechanisms throughout the load forecasting pipeline, reducing the technical threshold for non-expert users and enabling the integration of human experience. Our experiments demonstrate that the interactive load forecasting accuracy can be significantly improved when users provide proper insight in key stages. Our cost analysis shows that the framework remains affordable, making it practical for real-world deployment.