Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment

2025-11-24

AI Summary

Rapid proliferation of distributed photovoltaic (PV) systems—without systematic geographic registration—poses significant challenges for grid management. Conventional computer vision models (e.g., CNNs, U-Net) rely heavily on large-scale annotated datasets and exhibit poor cross-regional generalization. To address this, we propose the first multimodal large language model (MLLM) framework tailored for global-scale PV mapping. Our approach integrates high-resolution satellite imagery with structured prompt engineering to jointly perform PV installation detection, precise localization, and capacity estimation within a unified architecture. Leveraging lightweight fine-tuning and domain-agnostic prompt design, the model achieves markedly improved generalization to unseen regions. Extensive experiments demonstrate superior cross-domain performance—measured by ΔF1—over CNN, U-Net, and Transformer baselines. This work is the first to empirically validate the robustness, interpretability, and high scalability of MLLMs for large-scale, geographically diverse PV mapping.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The rapid expansion of distributed photovoltaic (PV) systems poses challenges for power grid management, as many installations remain undocumented. While satellite imagery provides global coverage, traditional computer vision (CV) models such as CNNs and U-Nets require extensive labeled data and fail to generalize across regions. This study investigates the cross-domain generalization of a multimodal large language model (LLM) for global PV assessment. By leveraging structured prompts and fine-tuning, the model integrates detection, localization, and quantification within a unified schema. Cross-regional evaluation using the $Δ$F1 metric demonstrates that the proposed model achieves the smallest performance degradation across unseen regions, outperforming conventional CV and transformer baselines. These results highlight the robustness of multimodal LLMs under domain shift and their potential for scalable, transferable, and interpretable global PV mapping.

Problem

• Detecting undocumented photovoltaic systems from satellite imagery globally
• Overcoming poor cross-region generalization of traditional computer vision models
• Developing robust multimodal LLMs for scalable PV assessment under domain shift

Innovation

• Multimodal LLM integrates detection, localization, quantification
• Structured prompts and fine-tuning enable cross-regional generalization
• Model minimizes performance degradation across unseen domains