Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

Understand

Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

• In order to bridge the huge gap between advanced machine learning forecasting models and actual power grid demand, this paper proposes an autonomous forecasting workflow based on LLMs.
• As a virtual analyst, the agent replaces tedious manual adjustments by autonomously analyzing data features, dynamically orchestrating optimal forecasting pipelines, and generating actionable analysis reports for decision-makers.
• The carefully arranged pipeline directly addresses the demand of the power grid through probabilistic forecasting for uncertainty quantification.