NEWS · RESEARCH · #136
Solar Intelligence — a hybrid retrieval-augmented platform for solar analytics, evidence-grounded QA, and forecasting
This arXiv cs.AI paper introduces Solar Intelligence, a hybrid retrieval-augmented system that unifies structured solar analytics, evidence-grounded scientific question answering, and machine-learning forecasting. The platform integrates daily NASA POWER and Biosphere 2 sensor data with a curated research corpus, uses DuckDB for structured queries, fuses BM25 and ChromaDB dense embeddings via Reciprocal Rank Fusion for retrieval with grounding by llama3.2:3b, produces daily irradiance/temperature/wind forecasts with XGBoost, and is exposed via FastAPI, Streamlit, and an MCP server for use as an app, API, or agent tool.
KEY POINTS
- This arXiv cs.AI paper introduces Solar Intelligence, a hybrid retrieval-augmented system that unifies structured solar analytics, evidence-grounded scientific question answering, and machine-learning forecasting.
- The platform integrates daily NASA POWER and Biosphere 2 sensor data with a curated research corpus, uses DuckDB for structured queries, fuses BM25 and ChromaDB dense embeddings via Reciprocal Rank Fusion for retrieval with grounding by llama3.2:3b, produces daily irradiance/temperature/wind forecasts with XGBoost, and is exposed via FastAPI, Streamlit, and an MCP server for use as an app, API, or agent tool.
- It matters because it combines structured data access, evidence‑grounded retrieval/QA, and forecasting in one domain-specific platform, addressing fragmentation in solar decision support tools.
WHY IT MATTERS
It matters because it combines structured data access, evidence‑grounded retrieval/QA, and forecasting in one domain-specific platform, addressing fragmentation in solar decision support tools.