Accurate forecasting of photovoltaic (PV) power is crucial for enhancing the reliability and economic viability of renewable energy systems. In this paper, we propose a novel hybrid architecture, GTCN, which synergistically integrates a Temporal Convolutional Network (TCN), a gated cross-attention mechanism, and a parallel Gated Recurrent Unit (GRU) decoder. The model efficiently captures both long-range temporal features and dynamic sequential dependencies in PV time series. Extensive experiments on real-world PV datasets demonstrate that our approach significantly outperforms traditional models such as LSTM, GRU, and standalone TCN in terms of forecasting accuracy and robustness.

Deep learning; Gated temporal convolutional network; Photovoltaic power prediction.