Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Energy & Utilities — Renewable Energy Systems, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new multi-source data fusion model is proposed for ultra-short-term solar irradiance prediction, crucial for photovoltaic system dispatch and power grid stability. This model addresses limitations of existing approaches, specifically their inability to capture spatial cloud dynamics, inadequately represent multi-scale cloud features, and use fixed low-frequency compensation. It employs InceptionNeXt to extract multi-scale, multi-directional spatial features from ground-based cloud images. A step-adaptive low-frequency compensation unit dynamically modulates global low-frequency information based on the prediction step. Enhanced image features are then combined with meteorological time-series features, and a TempAttnLSTM network captures global temporal dependencies for multi-step prediction. Experiments on the public NREL dataset and practical photovoltaic stations in Shandong demonstrate its effectiveness against several state-of-the-art methods.

Key takeaway

For Machine Learning Engineers developing ultra-short-term solar irradiance prediction systems, integrating multi-source data fusion with adaptive compensation is critical. This approach, which uses InceptionNeXt for multi-scale cloud features and TempAttnLSTM for temporal dependencies, can significantly improve forecasting accuracy and enhance power grid stability, especially under complex and dynamic cloud conditions. Consider adopting these techniques to overcome limitations of single time-series models.

Key insights

A multi-source fusion model with adaptive compensation improves ultra-short-term solar irradiance forecasting accuracy.

Principles

• Spatial cloud dynamics are crucial for accurate solar forecasting.
• Multi-scale feature extraction enhances cloud representation.
• Adaptive compensation improves prediction step flexibility.

Method

InceptionNeXt extracts multi-scale cloud features. A step-adaptive unit modulates low-frequency information. TempAttnLSTM fuses enhanced image and time-series data for multi-step prediction.

In practice

• Utilize InceptionNeXt for multi-scale cloud image analysis.
• Implement dynamic low-frequency compensation strategies.
• Combine image and meteorological time-series data.

Topics

• Solar Irradiance Forecasting
• Multimodal Fusion
• Cloud Feature Learning
• InceptionNeXt
• TempAttnLSTM
• Photovoltaic Systems

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.