In order to screen out the nonlinear relationship in massive load data more quickly and improve the accuracy of short-term load forecasting model

this paper focuses on the shortcomings of quantum particle swarm optimization algorithm（QPSO） in dealing with complex high-dimensional parameter optimization problems

and the genetic quantum particle swarm optimization algorithm（GAQPSO） combined with hybrid evolutionary theory of genetic algorithm（GA） is proposed to optimize the input weight and hidden layer deviation of the regular extreme learning machine

which forms a hybrid short-term load forecasting GAQPSO-ELM model. Meanwhile

when the input features are selected

the influences of historical load

temperature

time interval and type of date are fully considered

so the accuracy of the short-term load forecasting model is further improved. The experimental result shows that the proposed short-term load forecasting model has a higher accuracy than the QPSO-ELM model and the common ELM model

and it can better reflect the change trend of the daily load curve

which verifies the effectiveness of the proposed predictive model.