This invention discloses a comprehensive
energy load forecasting method based on a multi-scale graph conditional state-
space model. The method includes constructing a comprehensive
energy load forecasting dataset for a park, performing data preprocessing, and then using Pearson
correlation analysis to select highly correlated features; dividing the dataset into training, validation, and test sets, and standardizing the data; constructing a joint prediction model based on dynamic graph learning, a multi-scale graph conditional state-
space model, and a three-dimensional attention mechanism; training the joint prediction model using the
training set; adjusting hyperparameters and selecting the optimal model using the validation set; inputting the
test set into the trained model, and outputting the predicted
electricity, cooling, and heating loads; restoring the actual predicted values through inverse normalization; and evaluating the model performance using multiple indicators. This invention ensures the real-time requirement of the forecast and is suitable for online application scenarios in park energy dispatching.