Research on electricity consumption prediction based on Transformer and Improved memory mech-anism
In recent years,the rapid development of our country's economy has put forward higher require-ments for power allocation.To achieve efficient allocation of power resources requires more accurate power consumption forecasting.With the development of artificial intelligence,machine learning and other tech-nologies,efficient and accurate electricity consumption forecasting becomes possible.At present,Long Short-Term Memory(LSTM)and its variant models are commonly used in this field,but the accuracy of this method is relatively low.This paper proposes a power consumption prediction model based on improved memory mechanism and Transformer.The method uses a Transformer to encode the input and proposes a novel memory mechanism to realize predictions.Experiments show that compared with random forest regres-sion and LSTM and its variant models,the average error of this method decreases by 9.05％and 5.32％respectively within one week,and the model converges faster and has better generalization performance.
memory networkTransformertime series predictionmachine learningLong Short-Term Memory
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