随着电网中间歇性可再生能源发电规模的增大，为了保证电能质量和频率的稳定性，电网对可控资源的需求也随之增加。需求响应（demand response, DR）资源的灵活性已成为解决这一问题的一个有价值的方法。然而，目前关于DR资源的灵活性预测问题尚未得到充分的研究。本研究应用一种深度学习技术，即结合时间卷积神经网络的Transformer模型（temporal convolution network-combined transformer）来预测电动汽车与家庭热水系统两种DR资源的聚合灵活性。所提出的灵活性预测方法使用了基于DR资源的历史用电数据以及为了辅助预测所提出的DR信号数据。所提方法不仅可以预测聚合灵活性的大小，还可以预测其维持时间。最后，本文通过算例仿真验证了灵活性预测结果的准确性。仿真结果表明，在不同的灵活性维持时间下，DR资源灵活性的大小会发生变化。文中所提出的DR资源灵活性预测方法展现了其在释放需求侧资源的灵活性以向电网提供备用容量方面的应用。

With the growth of intermittent renewable energy generation in power grids, there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability. The flexibility of demand response (DR) resources has become a valuable solution to this problem. However, existing research indicates that problems on flexibility prediction of DR resources have not been investigated. This study applied the temporal convolution network (TCN)-combined transformer, a deep learning technique to predict the aggregated flexibility of two types of DR resources, that is, electric vehicles (EVs) and domestic hot water system (DHWS). The prediction uses historical power consumption data of these DR resources and DR signals (DS) to facilitate prediction. The prediction can generate the size and maintenance time of the aggregated flexibility. The accuracy of the flexibility prediction results was verified through simulations of case studies. The simulation results show that under different maintenance times, the size of the flexibility changed. The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.