The present work made an attempt on addressing problems of excessive uncertainties-induced low accuracy in station area electricity sale prediction,and proposed a prediction method based on modified ISSD optimized GRU neural network which uses reverse learning to improve searching efficiency of the SSD algorithm for optimal parameters.The GRU model was trained with influences data such as historical electricity sales,temperature,working day type and holi-day type of a certain distribution station,and ISSD optimization algorithm was used to realize optimal searching of number of hidden layer neurons and hyperparameters of learning rate,thereby an ISSD-GRU model for electricity sale prediction was established.The proposed ISSD-GRU model was indicated by case analysis to have improved accuracy for predicting station area electricity sales.
OBLSSD algorithmGRU neural networkelectricity sale predictiontime sequenceprediction accuracy
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