Autonomous AI Workflow for Energy Forecasting Integrates Large Language Models and Specialized Toolkits

Overview

This paper presents an autonomous forecasting workflow designed to bridge the gap between advanced machine learning models and the specific demands of power grid operations. The workflow utilizes Large Language Models (LLMs) to function as a virtual analyst, automating tasks typically requiring manual adjustment. Its core function involves analyzing data features, orchestrating forecasting pipelines, and generating analytical reports. The framework aims to align energy forecasting with practical power grid objectives, such as risk assessment and system reliability, by incorporating probabilistic forecasting for uncertainty quantification.

Research Context

Energy forecasting is identified as a critical component for power grid management. However, it presents distinct challenges compared to general time series analysis. Key differentiators include a high reliance on covariates, particularly meteorological factors, and the necessity for forecasting goals to align directly with operational aspects of the power grid. These operational aspects encompass risk assessment and ensuring system reliability. The research addresses a perceived gap between the capabilities of advanced machine learning forecasting models and the practical requirements of power grid demand.

Approach

The proposed approach centers on an autonomous forecasting workflow driven by LLMs. This LLM-based agent is designed to replace manual adjustments by performing several automated functions:

• Autonomous analysis of data features.
• Dynamic orchestration of optimal forecasting pipelines.
• Generation of actionable analysis reports for decision-makers.

The workflow's pipeline is structured to directly address power grid demands through probabilistic forecasting, which is utilized for uncertainty quantification. Furthermore, the framework operates as an automated testbed, enabling A/B testing of specific algorithmic plugins across various architectures to evaluate their empirical effectiveness.

The underlying toolkit, which forms the foundation of the algorithm, integrates a comprehensive set of components:

• 31 advanced temporal architectures.
• 6 customized exogenous modules.

This integration results in 146 highly configurable variants. To provide experiential references for the LLM agents, a large-scale benchmark was established across 21 energy datasets. Additionally, new high-quality renewable energy datasets, including meteorological factors, were released as part of this effort.

Findings

The research describes an autonomous forecasting workflow that integrates LLMs to act as a virtual analyst. This agent performs autonomous data feature analysis, dynamically orchestrates forecasting pipelines, and generates actionable reports. The pipeline specifically incorporates probabilistic forecasting to quantify uncertainty, directly addressing power grid operational requirements. The framework also functions as an automated testbed, facilitating A/B testing of algorithmic plugins across different architectures to assess their empirical performance. The foundational toolkit comprises 31 temporal architectures and 6 customized exogenous modules, yielding 146 configurable variants. A large-scale benchmark on 21 energy datasets was developed, and new renewable energy datasets with meteorological factors were released to support the LLM agents.

Why This Matters

The workflow aims to enhance decision-making in power grid operations by providing actionable analysis reports and aligning forecasting goals with practical needs such as risk assessment and system reliability. By automating data analysis and pipeline orchestration, it seeks to reduce the reliance on tedious manual adjustments. The integration of probabilistic forecasting offers quantification of uncertainty, a critical element for managing power grid stability.