# Solar Photovoltaic Assessment with Large Language Model

arXiv: https://arxiv.org/abs/2507.19144 · HTML: https://mlsanity.com/paper/2507.19144/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 61 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [RSGPT: A Remote Sensing Vision Language Model and Benchmark](https://mlsanity.com/paper/2307.15266/) (2023)
- [Large Language Models are Inconsistent and Biased Evaluators](https://mlsanity.com/paper/2405.01724/) (2024)
- [SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales](https://mlsanity.com/paper/2405.20974/) (2024)
- [Calibration in Deep Learning: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2308.01222/) (2023)
- [Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2112.07869/) (2021)
- [TopViewRS: Vision-Language Models as Top-View Spatial Reasoners](https://mlsanity.com/paper/2406.02537/) (2024)
- [A Survey on Evaluation of Multimodal Large Language Models](https://mlsanity.com/paper/2408.15769/) (2024)
- [Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2406.17169/) (2024)
- [A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization](https://mlsanity.com/paper/2406.09972/) (2024)
- [Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee](https://mlsanity.com/paper/2309.07157/) (2023)
- [DiffCLIP: Few-shot Language-driven Multimodal Classifier](https://mlsanity.com/paper/2412.07119/) (2024)

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

- [Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees](https://mlsanity.com/paper/2604.02788/) (2026)
- [AI and Open-data Driven Scalable Solar Power Profiling](https://mlsanity.com/paper/2605.02738/) (2026)
- [Modeling Time Series Dynamics with Fourier Ordinary Differential Equations](https://mlsanity.com/paper/2510.04133/) (2025)
- [Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment](https://mlsanity.com/paper/2511.19537/) (2025)
- [From Prediction to Diagnosis: Reasoning-Aware AI for Photovoltaic Defect Inspection](https://mlsanity.com/paper/2603.26776/) (2026)
- [Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment](https://mlsanity.com/paper/2605.28822/) (2026)
- [Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference](https://mlsanity.com/paper/2607.20480/) (2026)

## Abstract (arXiv)

Accurate detection and localization of solar photovoltaic (PV) panels in satellite imagery is essential for optimizing microgrids and active distribution networks (ADNs), which are critical components of renewable energy systems. Existing methods lack transparency regarding their underlying algorithms or training datasets, rely on large, high-quality PV training data, and struggle to generalize to new geographic regions or varied environmental conditions without extensive re-training. These limitations lead to inconsistent detection outcomes, hindering large-scale deployment and data-driven grid optimization. In this paper, we investigate how large language models (LLMs) can be leveraged to overcome these challenges. Despite their promise, LLMs face several challenges in solar panel detection, including difficulties with multi-step logical processes, inconsistent output formatting, frequent misclassification of visually similar objects (e.g., shadows, parking lots), and low accuracy in complex tasks such as spatial localization and quantification. To overcome these issues, we propose the PV Assessment with LLMs (PVAL) framework, which incorporates task decomposition for more efficient workflows, output standardization for consistent and scalable formatting, few-shot prompting to enhance classification accuracy, and fine-tuning using curated PV datasets with detailed annotations. PVAL ensures transparency, scalability, and adaptability across heterogeneous datasets while minimizing computational overhead. By combining open-source accessibility with robust methodologies, PVAL establishes an automated and reproducible pipeline for solar panel detection, paving the way for large-scale renewable energy integration and optimized grid management.