Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks
Summary: arXiv:2604.13455v1 Announce Type: cross
Abstract
Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer-based architectures, this paper challenges the prevailing “complexity-first” paradigm. We propose a lightweight, Physics-Informed Hybrid CNN-BiLSTM framework that prioritizes domain knowledge over architectural depth.
Introduction
The significance of precise solar irradiance forecasting cannot be overstated, especially as the demand for renewable energy sources continues to rise. Traditional methods often rely on complex deep learning architectures, which can be computationally intensive and less interpretable. This research introduces a novel approach that integrates physics-informed principles within a streamlined neural network framework.
Methodology
The proposed model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi-Directional Long Short-Term Memory (BiLSTM) network for capturing temporal dependencies. This hybrid architecture is explicitly guided by a vector of 15 engineered features, including:
- Clear-Sky indices
- Solar Zenith Angle
- Previous irradiance values
- Weather variables (temperature, humidity)
- Aerosol optical depth
Unlike standard data-driven approaches, which often depend solely on raw historical data, our model leverages physical insights to enhance predictive accuracy.
Hyperparameter Optimization
To ensure global optimality, hyperparameters are rigorously tuned using Bayesian Optimization techniques. This method allows for efficient exploration of the hyperparameter space, significantly improving model performance without the need for extensive computational resources.
Experimental Validation
We conducted experimental validation using NASA POWER data in Sudan. The results were striking: our physics-guided approach achieved a Root Mean Square Error (RMSE) of 19.53 W/m2, significantly outperforming complex attention-based baselines, which reported an RMSE of 30.64 W/m2.
Discussion
These results confirm what we term the “Complexity Paradox”: in high-noise meteorological tasks, explicit physical constraints offer a more efficient and accurate alternative to self-attention mechanisms. This finding advocates for a paradigm shift towards hybrid, physics-aware AI systems in the field of renewable energy management.
Conclusion
In conclusion, our study demonstrates that a Physics-Informed Hybrid CNN-BiLSTM framework can deliver superior performance in solar irradiance forecasting while remaining computationally efficient. This approach not only enhances predictive accuracy but also provides a more interpretable model that incorporates domain knowledge, paving the way for better real-time renewable energy management strategies.
Keywords
Solar Irradiance, Forecasting, Physics-Informed Neural Networks, CNN, BiLSTM, Renewable Energy Management