Investigating the Inductive Bias of Visual Convolutional Backbones for Multi-Step Photovoltaic Forecasting: A ConvNeXt–LSTM Approach

Accurate ultra-short-term forecasting of photovoltaic (PV) power is critical for maintaining grid stability and facilitating renewable energy integration. Although convolutional neural networks have demonstrated strong performance in computer vision, their effectiveness in time-series forecasting remains insufficiently validated. This study systematically evaluates a ConvNeXt–LSTM hybrid model for 15 min-resolution, 16-step-ahead (4 h) PV power forecasting. The results indicate that the proposed model outperforms the baseline LSTM, achieving reductions of 6.6% in MAE and 5.8% in RMSE, with statistical significance confirmed by the Wilcoxon signed-rank test (p < 0.05). However, the performance gains are highly dependent on architectural design, exhibiting sensitivity to kernel size and channel width, and showing diminishing returns under excessive scaling. These findings suggest a structural mismatch between vision-oriented convolutional inductive biases and temporal sequence characteristics. Furthermore, analyses of loss functions and feature degradation demonstrate consistent model ranking and enhanced robustness under reduced feature conditions. Overall, this study delineates the applicability boundaries of modern vision backbones in PV forecasting and provides practical guidance for model selection.