The accurate assessment of the demand response potential of air conditioning loads is a crucial foundational task for effectively scheduling their participation in demand response. To address the issue of low prediction accuracy caused by the traditional deep learning methods’neglect of the time-sequential distribution differences in real-world air conditioning loads

the concept of transfer learning to the time dimension is extended. The phenomenon of time-sequential distribution drift in air conditioning load time series is analyzed by drawing an analogy to covariate shift in transfer learning. Based on this

two time-sequential migration strategies

time-sequential distribution matching and time-sequential similarity quantification

are proposed. These strategies are integrated into the traditional recurrent neural network（RNN）architecture to build an adaptive RNN air conditioning load prediction model

thereby improving the prediction accuracy in real-world scenarios. Finally

an air conditioning load demand response potential evaluation method is proposed based on the overall approach of predicting the load values before and after the response and the adaptive RNN air conditioning load prediction model. Comparative experimental analysis on real datasets shows that this method can significantly improve the prediction accuracy of demand response potential over existing methods

thus providing effective reference for the demand response scheduling decisions of the grid dispatch center.