Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids. To capture intricate temporal relationships and enhance the precision of multi-step time forecast

this paper introduces an innovative approach for ultra-short-term photovoltaic (PV) power prediction

leveraging an enhanced Temporal Convolutional Neural Network (TCN) architecture and feature modeling. First

this study introduces a method employing the Spearman coefficient for meteorological feature filtration. Integrated with three-dimensional PV panel modeling

key factors influencing PV power generation are identified and prioritized. Second

the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features. Third

an enhanced TCN model is introduced

augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features. Meanwhile

a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences. Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models. The accuracy of PV power prediction in the next 4 hours

devoid of meteorological conditions

increases by 20.5%. Furthermore

incorporating shortwave radiation for predictions over 4 hours

2 hours

and 1 hour enhances accuracy by 11.1%

9.1%

and 8.8%

respectively.