Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecasting
arXiv:2604.04145v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and cloud motion, accurate forecasting requires effective modeling of complex spatiotemporal dependencies across multiple information sources. Although recent studies have advanced AI-based forecasting methods, most fail to fuse temporal observations, satellite imagery, and textual weather information in a unified framework. This paper proposes Solar-VLM, a large-language-model-driven framework for multimodal PV power forecasting. First, modality-specific encoders are developed to extract complementary features from heterogeneous inputs. The time-series encoder adopts a patch-based design to capture temporal patterns from multivariate observations at each site. The visual encoder, built upon a Qwen-based vision backbone, extracts cloud-cover information from satellite
arXiv:2604.04145v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and cloud motion, accurate forecasting requires effective modeling of complex spatiotemporal dependencies across multiple information sources. Although recent studies have advanced AI-based forecasting methods, most fail to fuse temporal observations, satellite imagery, and textual weather information in a unified framework. This paper proposes Solar-VLM, a large-language-model-driven framework for multimodal PV power forecasting. First, modality-specific encoders are developed to extract complementary features from heterogeneous inputs. The time-series encoder adopts a patch-based design to capture temporal patterns from multivariate observations at each site. The visual encoder, built upon a Qwen-based vision backbone, extracts cloud-cover information from satellite images. The text encoder distills historical weather characteristics from textual descriptions. Second, to capture spatial dependencies across geographically distributed PV stations, a cross-site feature fusion mechanism is introduced. Specifically, a Graph Learner models inter-station correlations through a graph attention network constructed over a K-nearest-neighbor (KNN) graph, while a cross-site attention module further facilitates adaptive information exchange among sites. Finally, experiments conducted on data from eight PV stations in a northern province of China demonstrate the effectiveness of the proposed framework. Our proposed model is publicly available at https://github.com/rhp413/Solar-VLM.
Executive Summary
The article introduces Solar-VLM, a novel multimodal framework leveraging vision-language models (VLMs) for enhanced photovoltaic (PV) power forecasting. The proposed model integrates three modality-specific encoders—time-series, visual (satellite imagery), and textual (weather descriptions)—to capture temporal, spatial, and contextual dependencies. A cross-site feature fusion mechanism, comprising a Graph Learner and cross-site attention module, addresses inter-station correlations. Empirical validation on data from eight PV stations in China demonstrates superior forecasting accuracy. The framework is open-source, fostering reproducibility and further research in renewable energy forecasting.
Key Points
- ▸ Introduces Solar-VLM, a multimodal VLM-driven framework for PV power forecasting, addressing the limitations of prior AI-based methods by integrating heterogeneous data sources (temporal, visual, and textual inputs).
- ▸ Develops modality-specific encoders: a patch-based time-series encoder for temporal patterns, a Qwen-based vision encoder for cloud cover analysis, and a text encoder for weather descriptor distillation.
- ▸ Proposes a cross-site feature fusion mechanism combining a Graph Learner (KNN-based graph attention network) and cross-site attention for adaptive inter-station information exchange, validated on eight PV stations in China.
- ▸ Open-source implementation (GitHub) ensures transparency, reproducibility, and community-driven improvements in solar forecasting research.
Merits
Multimodal Integration
Pioneers the fusion of temporal, visual, and textual data within a unified framework, overcoming the siloed approach of prior forecasting models and enabling richer spatiotemporal dependency modeling.
Architectural Innovation
Leverages advanced VLM components (e.g., Qwen-based vision backbone) and graph-based attention mechanisms, demonstrating state-of-the-art performance in cross-site feature fusion for renewable energy applications.
Practical Applicability
Validated on real-world data from eight geographically distributed PV stations, highlighting scalability and potential for deployment in grid operations and energy market participation.
Open Science Commitment
Provides an open-source implementation, democratizing access to cutting-edge forecasting tools and accelerating interdisciplinary collaboration in solar energy research.
Demerits
Data Dependency
Performance heavily relies on the quality and granularity of input data (e.g., satellite imagery resolution, weather textual descriptions), which may limit applicability in regions with sparse or low-resolution data infrastructure.
Computational Complexity
The multimodal fusion and cross-site attention mechanisms introduce significant computational overhead, potentially hindering real-time forecasting in resource-constrained environments.
Generalizability Concerns
Validation on a single dataset (eight stations in one Chinese province) raises questions about the model's adaptability to diverse climatological conditions, PV system configurations, or regional weather patterns.
Textual Data Limitations
The reliance on textual weather descriptions may introduce ambiguity or noise, particularly if historical weather narratives are inconsistently formatted or lack granularity.
Expert Commentary
Solar-VLM represents a significant advancement in the application of multimodal AI to renewable energy forecasting, addressing a critical gap in the literature by unifying temporal, spatial, and contextual data sources. The integration of vision-language models with graph-based attention mechanisms is particularly innovative, offering a scalable solution for capturing complex interdependencies in distributed PV systems. However, the framework's reliance on high-quality multimodal data and significant computational resources may pose barriers to widespread adoption, particularly in regions with limited data infrastructure or under resource-constrained operational environments. The open-source release is commendable and aligns with the growing demand for transparency in AI-driven energy systems, but future work should focus on addressing interpretability challenges to ensure regulatory acceptance. Additionally, while the validation on eight stations is promising, broader testing across diverse climatological and PV system configurations is essential to establish the model's generalizability. The work sets a new benchmark for solar forecasting and signals a paradigm shift toward holistic, data-driven approaches in renewable energy management.
Recommendations
- ✓ Expand validation to include diverse datasets across multiple regions and PV system types to assess generalizability and robustness under varying climatological conditions and system configurations.
- ✓ Incorporate explainability layers (e.g., attention maps, feature attribution techniques) to enhance interpretability and facilitate regulatory compliance, particularly for high-stakes grid operations and market participation.
- ✓ Optimize computational efficiency through model pruning, quantization, or edge computing adaptations to enable real-time forecasting in resource-constrained environments, such as remote PV plants or microgrids.
- ✓ Develop standardized data pipelines for multimodal inputs (satellite imagery, textual weather reports) to mitigate data quality issues and ensure consistency across applications.
- ✓ Explore federated learning approaches to enable collaborative model training across multiple PV sites without sharing raw data, addressing privacy concerns and data sovereignty issues in decentralized energy systems.
Sources
Original: arXiv - cs.AI
