Abstract: Accurately predicting the multi-energy load of electrical, cooling and heating in the integrated energy system(IES) is the key to improving the comprehensive efficiency of various energy sources and obtaining greater economic benefits. Therefore, a method for short-term prediction of integrated energy load based on time series feature grayscale map and multi-task learning is proposed. Firstly, the correlation analysis and redundancy analysis of the IES time series feature data set are carried out through the initial feature set through the fast correlation-based filter(FCBF) improved by the maximum information coefficient(MIC). Secondly, the feature selection result is reconstructed into a time series feature grayscale image using the factor reconstruction method and the MIC-gamma image enhancement method, which can intuitively and effectively reflect the feature correlation of the actual data. Then, the CBAM-CNN-DBiGRU network based on the multi-task learning framework is used for training. The embedded convolutional block attention module(CBAM) and the deep bidirectional gated recurrent unit(DBiGRU) structure can effectively strengthen the key information extraction and time series information processing capabilities of the shared layer. Finally, the method proposed in the paper is tested by taking the IES data of Arizona State University as an example. Selecting typical working days and typical rest days and comparing various deep network models, the test results show that the weighted average absolute percentage error and weighted root mean square error of the model on typical working day is respectively reduced by a maximum of 0.881 3% and 229.259 3 kW, and on typical rest day is respectively reduced by a maximum of 0.994 2% and 360.800 7 kW, which can effectively improve the accuracy of IES multi-energy load prediction.