A Photovoltaic Power Forecasting Method Integrating Physical Mechanisms and Deep Learning

Why It Matters

Accurate solar forecasts reduce grid instability and enable higher renewable penetration, directly impacting utility economics and reliability. The method’s adaptive fusion offers a scalable solution for operators facing volatile weather conditions.

Key Takeaways

• Non‑uniform error compensation improves physical model across irradiance ranges
• 37‑dimensional feature set blends meteorology, time, physics, interactions
• Parallel multi‑scale CNN with BiLSTM captures diverse temporal patterns
• Dynamic fusion uses four confidence dimensions for adaptive weighting

Pulse Analysis

The rapid growth of distributed photovoltaic (PV) installations has intensified the need for precise power forecasts. Traditional physics‑based models provide interpretability but falter under complex, rapidly changing weather, while pure data‑driven techniques often overfit and lack robustness during extreme events. Industry stakeholders therefore seek hybrid solutions that can combine the strengths of both paradigms, ensuring reliability for grid dispatch and market operations.

The proposed method tackles these challenges through three core innovations. First, a non‑uniform error compensation strategy segments irradiance levels, allowing the physical branch to correct systematic biases across low‑ and high‑light conditions. Second, a 37‑dimensional feature suite merges meteorological readings, temporal markers, physics‑derived calculations, and statistical interactions, feeding a parallel multi‑scale convolutional neural network (kernel sizes 3, 7, 15) linked to a BiLSTM layer for nuanced temporal extraction. Third, a dynamic weighted fusion mechanism evaluates confidence across irradiance, temperature, time of day, and weather stability, adaptively balancing the physical and neural contributions.

Empirical results from an annual, 15‑minute dataset demonstrate substantial gains: mean absolute error drops to 3.571 kW, root‑mean‑square error to 6.384 kW, and the coefficient of determination climbs to 0.9781. These improvements translate to tighter scheduling margins, reduced reserve requirements, and lower operational costs for utilities. As solar penetration deepens, such adaptive hybrid models will become essential tools for maintaining grid stability, informing market pricing, and guiding future investments in renewable integration.