A Multimodal Transformer Framework with State-Aware Adaptive Fusion for Ultra-Short-Term Solar Irradiance Forecasting with Sky Cloud Images
空画像を用いた超短期太陽放射予測のための状態認識適応融合を備えたマルチモーダルトランスフォーマーフレームワーク (AI 翻訳)
Zhao H, Song A, Jin Y, Yu H
🤖 gxceed AI 要約
日本語
本論文は、空画像と数値系列を統合したマルチモーダル深層学習フレームワークを提案し、超短期の太陽放射予測精度を向上させる。状態認識型の適応融合機構により、気象情報と過去の物理パターンを動的に統合し、従来の単一モーダル手法を上回る性能を示した。再生可能エネルギーの出力変動予測に貢献する。
English
This paper proposes a multimodal deep learning framework integrating sky cloud images and numerical sequences to improve ultra-short-term solar irradiance forecasting. A state-aware adaptive fusion mechanism dynamically integrates visual weather information with historical physical patterns, outperforming single-modal baselines. It contributes to renewable energy output variability prediction.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では再生可能エネルギーの導入拡大に伴い、太陽光発電の出力予測精度向上が系統安定化に不可欠である。本手法は、気象データとAIを組み合わせた予測技術として、電力会社や発電事業者の運用効率化に寄与し、FIT制度下でのインバランス回避にも有効である。
In the global GX context
Globally, accurate solar irradiance forecasting is critical for integrating high shares of variable renewables into power grids. This work advances AI-based forecasting methods, aligning with global efforts to enhance grid stability and optimize renewable energy utilization, relevant to energy transition policies and grid operators worldwide.
👥 読者別の含意
🔬研究者:Provides a novel multimodal fusion approach for solar forecasting, useful for advancing AI applications in renewable energy prediction.
🏢実務担当者:Offers a practical method to improve solar power output forecasting, aiding in grid management and trading strategies.
🏛政策担当者:Highlights the importance of investing in AI-driven forecasting to support renewable integration and grid stability.
📄 Abstract（原文）
<title>Abstract</title> <p>As renewable energy penetration continues to increase, accurate ultra-short-term solar irradiance forecasting is essential for improving energy utilization and supporting power system operation. However, irradiance variations are jointly influenced by historical trends and rapid cloud changes, making traditional single-modal methods insufficient for capturing complex dynamics under varying weather conditions. This paper proposes a multimodal solar irradiance forecasting framework based on the collaborative representation of sky cloud images and numerical sequences. The framework introduces a heterogeneous modal state encoding mechanism and a state-aware adaptive selective attention fusion mechanism to enhance interactions between visual weather information and historical physical patterns. Specifically, the numerical modality captures irradiance trends and temporal correlations through dependency modeling, while the image modality extracts cloud structures and evolution features through spatiotemporal representation. The proposed fusion mechanism dynamically adjusts modality contributions according to forecasting conditions, enabling effective integration of complementary information. Experimental results demonstrate that the proposed framework outperforms benchmark models in forecasting accuracy and robustness, validating the effectiveness of combining visual cloud information with numerical evolution patterns for ultra-short-term solar irradiance forecasting.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10464830/v1first seen 2026-08-05 04:31:59
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