Load forecasting is an important machine learning problem in the field of power system, which is of great significance to power system source load balance, power supply planning and system maintenance. With the development of power technology, fine-grained load data become more easily available, which puts forward new requirements for load forecasting accuracy. In this paper, we use a deep learning framework, namely conditional autoencoder, to forecast day-ahead load. And the conditional autoencoder extracts the characteristics of load time series through deep neural network and finds the sequence pattern of historical data. In addition, we use the load data of different time scales for training, so as to improve the accuracy of the model. In this paper, the real data of regenerative electric boiler in Jilin Province are used for load forecasting, and the effectiveness of the model is verified.