<b>Algorithm Pseudocode</b>

Below is the pseudocode for the TCN-DBN hybrid model proposed in this study. The pseudocode outlines the complete process, including data preprocessing, TCN module processing, DBN module processing, and the integration of both modules.The above pseudo-code details the implementation process of the TCN-DBN hybrid model proposed in this study. The whole process includes data preprocessing, TCN module processing, DBN module processing, and integration of the two modules. The model generates point forecasts and forecast interval boundaries for short-term loads, providing important support for risk quantification and decision-making in power systems. The pseudo-code follows standard Python syntax specifications for functions and loops and is easy to understand and implement. The organization logic of the functions reflects the various steps in the model training and forecasting process.