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Life sciences · Preprint
arXiv · September 10, 2026
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SolCloudLLM is a novel multimodal machine learning framework that combines sky imagery and time-series data via large language models for short-term solar irradiance forecasting. The framework shows computational improvements (25.4% maximum relative MSE reduction) on two research datasets, particularly under cloudy conditions and in few-shot scenarios, but lacks peer review, operational validation, and comparison to deployed forecasting standards.
Preprint. Research datasets (SIRTA and SKIPP'D) used for benchmarking; no information on real-world grid operators, sites, or temporal scope.. Intervention: SolCloudLLM: bidirectional multimodal fusion framework combining sky-image patches and time-series patches aligned and fused through an LLM embedding space for solar irradiance forecasting.. Compared with: Baseline methods (unspecified deep learning methods and non-learning physical methods); specific baseline names and configurations not provided in abstract..
Maximum relative MSE reduction of 25.4% across all forecasting horizons on SIRTA and SKIPP'D datasets Multimodal fusion benefits concentrated primarily under cloudy conditions SolCloudLLM achieves best performance in nearly all few-shot settings
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This is a machine learning methods paper presenting a novel computational framework for solar forecasting without clinical or regulatory validation, peer review, or comparison against established operational standards in energy systems.
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Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecasting horizons, errors are predominantly driven by cloud induced ramps: relying solely on historical numerical data may struggle to anticipate an incoming cloud, making ground-based sky images a crucial complementary physical signal. Furthermore, forecast performance is highly sensitive to location and local observing conditions, creating a strong need for site-specific data that are often scarce. Recently, large language models (LLMs) have demonstrated competitive performance and high data efficiency in time-series forecasting. Despite their success, existing LLM-based forecasting methods remain predominantly unimodal, relying primarily on historical numerical time-series data. Effectively incorporating sky imagery into an LLM-based forecasting framework remains under-explored and an open challenge. In this paper, we propose SolCloudLLM, an LLM-based multimodal forecasting framework. SolCloudLLM aligns sky-image patches with time-series patches and fuses their corresponding representations through bidirectional multimodal fusion, yielding a unified representation that is subsequently mapped into the embedding space of an LLM. Extensive experiments on the SIRTA and SKIPP'D datasets demonstrate that SolCloudLLM consistently outperforms the best baseline methods in MSE across all forecasting horizons, achieving a maximum relative MSE reduction of 25.4%. Stratified analysis further indicates that the benefits of multimodal fusion are concentrated primarily under cloudy conditions. Notably, SolCloudLLM achieves the best performance in nearly all few-shot settings, whereas other deep learning baselines experience substantial performance degradation and are frequently outperformed by the non-learning physical method.
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