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

תקציר מקורי באנגליתarXiv:2307.07191v3 Announce Type: replace Abstract: 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 t