Peng Chen

DST-CAN: A dynamic spatiotemporal convolutional attention network for short-term wind power forecasting

A hierarchical hybrid spatiotemporal architecture is introduced, which synergistically utilizes dilated causal convolutions to extract local trends and instantaneous fluctuations, while incorporating a multi-head self-attention mechanism to aggregate global context information, thereby achieving a complementary fusion of local and global features.