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

2026-06-04 · Source: Artificial Intelligence · 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, which include single time-series models failing to capture spatial cloud dynamics, standard convolutions inadequately representing multi-scale cloud features, and fixed low-frequency compensation strategies lacking adaptability to different prediction steps. The proposed method utilizes InceptionNeXt to extract multi-scale, multi-directional spatial features from ground-based cloud images. It then integrates a step-adaptive low-frequency compensation unit to dynamically modulate global low-frequency information based on the specific prediction step. Finally, these enhanced image features are combined with meteorological time-series features, and a TempAttnLSTM network captures global temporal dependencies for robust 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 forecasting systems, you should consider integrating multi-source data fusion with adaptive compensation. Implementing an InceptionNeXt-based approach for multi-scale cloud feature extraction and a step-adaptive low-frequency compensation unit can significantly improve prediction accuracy and grid stability. This method, validated on NREL data, offers a robust framework for handling complex cloud dynamics and multi-step predictions.

Key insights

A multi-source fusion model improves ultra-short-term solar forecasting by adapting to cloud dynamics and prediction steps.

Principles

• Spatial cloud dynamics are critical for accuracy.
• Prediction step-adaptive compensation is vital.
• Multi-source data fusion improves robustness.

Method

The model extracts multi-scale cloud features via InceptionNeXt, applies step-adaptive low-frequency compensation, and fuses these with meteorological time-series into a TempAttnLSTM for multi-step prediction.

In practice

• Use InceptionNeXt for cloud image feature extraction.
• Implement dynamic low-frequency compensation.
• 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 Artificial Intelligence.