Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, AI for Renewable Energy Systems · Depth: Expert, quick

Summary

A new study proposes a lightweight, Physics-Informed Hybrid CNN-BiLSTM framework for accurate Global Horizontal Irradiance (GHI) forecasting, challenging the trend of computationally expensive Transformer-based architectures. This model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi-Directional LSTM for capturing temporal dependencies. Unlike standard data-driven methods, the framework is guided by 15 engineered features, including Clear-Sky indices and Solar Zenith Angle, rather than relying solely on raw historical data. Rigorous hyperparameter tuning using Bayesian Optimization was applied. Experimental validation using NASA POWER data in Sudan demonstrated an RMSE of 19.53 W/m^2, significantly outperforming attention-based baselines which achieved an RMSE of 30.64 W/m^2. These results suggest that explicit physical constraints are more efficient and accurate in high-noise meteorological tasks.

Key takeaway

For AI Engineers developing renewable energy forecasting systems, this research indicates that incorporating physics-informed features and hybrid CNN-BiLSTM architectures can yield superior accuracy and efficiency compared to complex Transformer models. You should consider prioritizing domain knowledge integration to achieve better performance, especially in high-noise environments like arid regions, potentially reducing computational costs while improving grid stability.

Key insights

Physics-guided hybrid neural networks outperform complex self-attention models in noisy solar irradiance forecasting.

Principles

• Domain knowledge enhances model accuracy.
• Complexity does not always equate to performance.

Method

The proposed method combines CNN for spatial features and Bi-LSTM for temporal dependencies, explicitly guided by 15 engineered physical features, with Bayesian Optimization for hyperparameter tuning.

In practice

• Integrate clear-sky indices into forecasting models.
• Utilize Bayesian Optimization for hyperparameter tuning.

Topics

• Solar Irradiance Forecasting
• Physics-Guided Neural Networks
• Hybrid CNN-BiLSTM
• Global Horizontal Irradiance
• Renewable Energy Management

Best for: AI 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.