This invention discloses an end-to-end unit
combination method for power systems driven by a spatiotemporal attention graph neural network based on physical data fusion. The method includes: acquiring power
system operating data; constructing a spatiotemporal self-attention graph
convolutional neural network (SINNN), and building a unit combination model based on the SINNN; inputting the power
system operating data into the unit combination model for two-stage model training; the first stage guides
model parameter initialization based on a fusion strategy of physical mechanisms, local relaxation of integer variables, and supervised pre-training; the second stage uses goal guidance, physical mechanism constraints, and local relaxation of integer variables to achieve secondary adjustment of
model parameters; introducing an adaptive penalty term update
algorithm to obtain the trained unit combination model; and realizing power
system unit combination decisions based on the trained unit combination model. This invention can effectively
handle the temporal and spatial constraints in the unit combination problem, achieving rapid end-to-end unit combination decisions.