用大模型代理融合物联网数据，提升风电光伏预测精度与决策支持能力
LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support
- 构建六层框架整合多源传感数据与大模型推理
- 提出12个开放挑战，涵盖实时部署与不确定性量化
- 适合能源系统研究者和智能电网开发者参考
可再生能源发电的可靠预测是电网稳定、能源交易、电池调度及碳感知运营规划的基础。太阳能和风能资源具有固有的间歇性，其输出受云量、风速、大气湍流、季节变化和局部地形影响。物联网和边缘设备（如智能电表、逆变器、风速计、辐射计、气象站和电网接口传感器）的普及，产生了前所未有的实时运行数据，传统预测流程难以充分利用。本综述探讨了大语言模型（LLM）代理如何通过整合异构传感器数据、天气API、历史发电记录、电网约束和上下文推理，提升可再生能源预测能力。我们调研了经典方法、统计时间序列模型、深度学习架构、物理融合方法及新兴的LLM代理框架在解释性、不确定性处理、通信与操作员引导方面的应用。提出一个涵盖数据采集、预处理、特征工程、模型推断、不确定性估计和自然语言报告的六层分类体系。识别出十二个开放挑战，包括实时部署、模型漂移、分布外偏差、LLM幻觉控制、边缘硬件互操作性以及与能源管理系统集成。文章最后建议以开放基准、物理信息驱动的LLM训练和联邦预测架构为核心的科研方向。
原文摘要 · Abstract (English)
Reliable forecasting of renewable energy generation is a foundational requirement for grid stability energy trading battery scheduling and carbon aware operational planning Solar and wind resources are inherently intermittent their output fluctuates with cloud cover wind speed atmospheric turbulence seasonal patterns and local terrain The proliferation of IoT and edge devices spanning smart meters inverters anemometers pyranometers weather stations and grid interface sensors has created an unprecedented volume of real time operational data that conventional forecasting pipelines are ill equipped to exploit fully This review investigates how large language model LLM agents can enhance renewable energy forecasting by integrating heterogeneous sensor streams weather API data historical generation records grid constraints and contextual reasoning into unified decision support workflows We survey classical forecasting methods statistical time series models deep learning architectures physics hybrid approaches and emerging LLM agent frameworks for explanation uncertainty communication and operator guidance A six layer taxonomy is proposed covering data acquisition preprocessing feature engineering model inference uncertainty estimation and natural language reporting The review identifies twelve open challenges spanning real time deployment model drift under distribution shift uncertainty quantification hallucination control in LLM agents interoperability of edge hardware and integration with energy management systems The paper concludes by recommending a research agenda centred on open benchmarks physics informed LLM grounding and federated forecasting architectures
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