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

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. Furthermore, the framework acts as an automated testbed, enabling seamless A/B testing of specific algorithmic plugins across various architectures to evaluate their empirical effectiveness. As the foundation of the algorithm, the underlying toolkit integrates 31 advanced temporal architectures and 6 customized exogenous modules, resulting in 146 highly configurable variants. In addition, to provide experience references for the agents, we established a large-scale benchmark on 21 energy datasets and released new high-quality renewable energy datasets with meteorological factors.