Accurate and reliable wind power forecasting has become very important to power system scheduling and safely stable operating. In this paper, a novel self-attention temporal convolutional network (SATCN) is combined with long-short term memory (LSTM) to forecast wind power for guaranteeing the continuous electricity supply. In the proposed SATCN-LSTM model, the structure of SATCN with a self-attention mechanism is conducted to pay more attention to features that contribute more to the output. The strength of SATCN is performed through extracting temporal feature of meteorological data and correlation characteristics between variables. LSTM is used after SATCN to further build the connection between features and outputs for predicting future ultra-short time wind power. The effectiveness and advancement of the proposed method is tested by using meteorological data and wind power data from two different wind farms in the U.S. The experimental results reveal that the SATCN-LSTM model is more accurate comparing to other methods. Taking California's fourth quarter wind power forecast results as an example, the proposed method has carried out a reduction of 17.56%, 10.99%,11.34% and 3.68% on the root mean square error compared with LSTM, TCN, CNN-LSTM, TCN-LSTM.

准确可靠的风电功率预测对于电力系统调度和安全稳定运行变得非常重要。在本文中，一种新颖的自注意力时间卷积网络（SATCN）与长短期记忆（LSTM）相结合来预测风电，以保证持续供电。在提出的SATCN-LSTM模型中，SATCN的结构采用了自注意力机制，更加关注对输出贡献更大的特征。SATCN的强度是通过提取气象数据的时间特征和变量之间的相关特征来实现的。LSTM 在 SATCN 之后被用来进一步建立特征和输出之间的联系，以预测未来的超短时风电。通过使用来自美国两个不同风电场的气象数据和风电数据测试了所提出方法的有效性和先进性。实验结果表明，SATCN-LSTM模型相比其他方法更准确。以加州第四季度风电功率预测结果为例，所提方法与LSTM、TCN、CNN-LSTM、TCN相比，均方根误差分别降低了17.56%、10.99%、11.34%和3.68%。 LSTM。