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

arXiv: https://arxiv.org/abs/2307.07191 · HTML: https://mlsanity.com/paper/2307.07191/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 7 of 7)

- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation](https://mlsanity.com/paper/2410.10393/) (2024)
- [fev-bench: A Realistic Benchmark for Time Series Forecasting](https://mlsanity.com/paper/2509.26468/) (2025)
- [Zero-Shot Time Series Forecasting with Covariates via In-Context Learning](https://mlsanity.com/paper/2506.03128/) (2025)
- [KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning](https://mlsanity.com/paper/2605.30002/) (2026)
- [Are Time-Indexed Foundation Models the Future of Time Series Imputation?](https://mlsanity.com/paper/2511.05980/) (2025)
- [RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models](https://mlsanity.com/paper/2607.06504/) (2026)

## Abstract (arXiv)

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.