Research Paper#Solar Energy Forecasting, Deep Learning, Time Series Analysis🔬 ResearchAnalyzed: Jan 3, 2026 15:59
Transformer Dominates Solar Irradiance Forecasting in Ho Chi Minh City
Published:Dec 29, 2025 23:22
•1 min read
•ArXiv
Analysis
This paper provides a valuable benchmark of deep learning architectures for short-term solar irradiance forecasting, a crucial task for renewable energy integration. The identification of the Transformer as the superior architecture, coupled with the insights from SHAP analysis on temporal reasoning, offers practical guidance for practitioners. The exploration of Knowledge Distillation for model compression is particularly relevant for deployment on resource-constrained devices, addressing a key challenge in real-world applications.
Key Takeaways
- •Transformer architecture excels in short-term solar irradiance forecasting.
- •SHAP analysis reveals distinct temporal reasoning strategies among architectures.
- •Knowledge Distillation effectively compresses the Transformer model while potentially improving accuracy.
- •The study focuses on a specific geographic location (Ho Chi Minh City) using high-resolution satellite data.
Reference
“The Transformer achieved the highest predictive accuracy with an R^2 of 0.9696.”