发表机构
University of Arizona（亚利桑那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Solar Intelligence，一个混合检索增强框架，统一结构化太阳能分析、基于证据的科学问答和机器学习预测，集成多源数据，提供每日辐照度、温度和风速预测，支持多种使用方式。
AI 中文摘要
太阳能决策支持分散在多个仪表板中，这些仪表板提供数据但不提供解释；研究论文解析缓慢；通用语言模型并非太阳能领域专用，且回答缺乏证据。本文介绍了Solar Intelligence，一个混合检索增强框架，将结构化太阳能分析、基于证据的科学问答和机器学习预测统一到一个系统中。该平台集成了NASA POWER每日太阳能和气象数据、生物圈2号地面传感器读数，以及经过整理的论文集和机构报告。结构化查询使用DuckDB SQL；科学问题由混合检索器回答，该检索器通过倒数排名融合将BM25和ChromaDB密集嵌入相结合，并通过语言模型（llama3.2:3b）进行基于证据的回答。极端梯度提升（XGBoost）模型生成每日辐照度、温度和风速预测。该系统通过FastAPI、Streamlit和MCP服务器暴露，因此可被学生、研究人员和能源分析师用作应用程序、API服务或智能体工具。
英文摘要
Solar energy decision support is fragmented across dashboards that provide data without explanation, research papers are slow to parse, and general-purpose language models are not solar domain specific and answer without evidence. This paper introduces Solar Intelligence, a hybrid retrieval-augmented framework that unifies structured solar analytics, evidence-grounded scientific question answering, and machine learning forecasting in one system. The platform integrates daily NASA POWER solar and meteorological data, Biosphere 2 ground-sensor readings, and a curated corpus of research papers and institutional reports. Structured queries use DuckDB SQL; scientific questions are answered by a hybrid retriever that fuses BM25 and ChromaDB dense embeddings via Reciprocal Rank Fusion, with responses grounded through a language model (llama3.2:3b). An Extreme Gradient Boosting (XGBoost) model produces daily forecasts of irradiance, temperature, and wind speed. The system is exposed via FastAPI, Streamlit, and an MCP server, so it can be used as an application, an API service, or an agent tool - by students, researchers, and energy analysts.
Comments15 pages, 2 figures, 4 tables
