Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field

Accurate and robust short-term wind power forecasting (WPF) is of great significance to enhance the rate of renewable energy utilization in power systems and to promote low-carbon energy transformation. However, the high randomness and complex volatility of wind power bring great challenges when designing reliable and accurate forecasting models. In this paper, a novel hybrid model based on singular spectrum analysis (SSA) and a temporal convolutional attention network with an adaptive receptive field (ARFTCAN) is proposed. Specifically, to ensure the sufficiency and completeness of feature decomposition and reconstruction, we develop an SSA-based component partitioning mechanism to decompose complex original wind power sequences and determine their trend, period and noise components. Moreover, a self-attention mechanism and the adaptive receptive field (ARF) algorithm are integrated into a temporal convolutional network (TCN) to ensure the automatic extraction of multiple critical frequency-domain features within the complete fluctuation period. Furthermore, the forecasting results obtained with different feature components are integrated into the final model to realize identification, reconstruction and extrapolation from a multifrequency-domain perspective. The results demonstrate that the proposed model effectively supports the adaptability of short-term WPF in four seasons. Especially in scenarios with high-frequency wind power fluctuations, the mean absolute percentage error (MAPE) of the proposed model is reduced by more than 52% relative to those of the state-of-the-art decomposition-forecasting models. Moreover, compared to the classic SSA-based deep learning models, the proposed model achieves an MAPE reduction of over 13% in a scenario with low power output.