Accurate integrated energy load forecasting is a key prerequisite for the preliminary planning and subsequent on-demand coordinated operation of regional integrated energy systems. The recent Transformer-based method has shown significant potential in long sequence forecasting for its excellent global modeling capabilities. However, the permutationally invariant self-attention mechanism in Transformer leads to the loss of temporal information and ignores the key dependencies between different variables in multi-energy load forecasting. To address the above challenges, this study proposes a patch and variable mixing model (PVMM) to achieve accurate multi-energy load forecasting. PVMM uses patch embedding technology to convert the input multi-energy load sequence into a 3D vector, thereby retaining the temporal and variable information of the patch. Secondly, this study proposes a patch mixing module (PMM) based on deep separable convolution to establish a temporal dependency model. In addition, this study also proposes a variable dynamic projection attention module (VDP-AM) to map Query and Value variables to a higher dimension and handle the interaction between multiple variables through a self-attention mechanism. Finally, the prediction accuracy and generalization ability of this method on the online system dataset publicly available at Arizona State University surpass existing methods.