Solar Intelligence

Jyotsna Singh

cs.AISep 12, 2026

Solar Intelligence

Authors: Jyotsna Singh

Organizations: College of Information Science, University of Arizona, Tucson, AZ, USA

Abstract

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.

Figures & tables

S. No.	Data Details	Data Period	Data Sources
1	Research papers and Reports	1994–2006	arXiv, NREL, IEA-PVPS,
			IRENA, and Sandia
2	Irradiance, Temperature, Humidity,	2012–2026	NASA POWER
	Wind Speed, Precipitation
3	Air Temperature, Irradiance, and	Aug 2012 –	University of Arizona
	Wind Speed	July 2013	Biosphere 2

Table 1: Data sources used in Solar Intelligence

	Category	Topic Query	Max Results
1	Forecasting & ML	solar irradiance forecasting machine learning	20
		solar power generation neural network	15
2	Irradiance components	global horizontal irradiance GHI estimation	15
		direct normal irradiance DNI clear sky	10
		solar radiation satellite measurement validation	15
		solar resource assessment NASA POWER	10

Table 2: Eighteen topic queries used to construct the arXiv subset of the corpus via the official arXiv API

S. No.	Features	Types
1	Day-of-year (1–366)	Calendar features
2	Month (1–12)
3	$\sin(2\pi\cdot\mathrm{doy}/365.25)$	Cyclic seasonality
4	$\cos(2\pi\cdot\mathrm{doy}/365.25)$
5	Target value at lag 1 day	Lag features
6	Target value at lag 7 days

Table 3: Features used in forecasting

Component	Technology
Backend	FastAPI
Storage	DuckDB
Sparse retrieval	BM25
Dense retrieval	BGE-small
Fusion	Reciprocal Rank Fusion
Chunking	PyMuPDF

Table 4: System component and technology stack of Solar Intelligence

Figure 1: NASA POWER vs Biosphere 2 (B2) (32.578 ∘ N, 110.848 ∘ W) (Aug 2012 – July 2013).

Panel of Figure 2 — Figure 2: Seven-day forecasts from the Solar Intelligence Streamlit dashboard for temperature (top), temperature (middle), and irradiance(bottom) at Biosphere 2 location. Each panel shows the last observed NASA POWER value at the top and the XGBoost roll-forward prediction as a continuous line (32.578 ∘ N, 110.848 ∘ W).

Solar Power Forecasting

Solar power forecasting predicts photovoltaic generation and irradiance from weather, imagery, plant data, and recent output to support reliable grid and energy decisions.

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Apr 23, 2026J/K move · Enter open · S save

Forecasting Solar Energy Using a Single Image

Jeremy Klotz, Shree K. Nayar

Computer Science Department, Columbia University, 500 West 120th St., New York, 10027, NY, USA

Solar Irradiance Forecasting Solar Power Forecasting

Open paper AskPDF arXiv

Solar panels are increasingly deployed in cities on rooftops, walls, and urban infrastructure. Although the panel costs have fallen in recent years, the soft costs of installing them have not. These soft costs include assessing the illumination (irradiance) of a panel, which is typically performed using a 3D model that fails to capture small nearby structures that impact the irradiance. Our approach uses a single image taken at the panel's location to forecast its irradiance at any time in the future. We use visual cues in the image to find the camera's orientation and the portion of the sky visible to the panel in order to forecast the irradiance due to the sun and the sky. In addition, we show that the irradiance due to reflections from nearby buildings varies smoothly over time and can be forecasted from the image. This approach enables assessing the solar energy potential of any surface and forecasting the temporal variation of a panel's irradiance. We validate our approach using real irradiance measurements in urban canyons. We show that our approach often yields more accurate irradiance forecasts compared to conventional irradiance-based transposition methods and 3D model-based simulations. We also show that a single spherical image can be used to find the best fixed orientation of a panel. Finally, we present Solaris, a device to capture the image seen by a panel in a variety of urban settings.

Abstract

Solar Power Forecasting

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