Electric quantity prediction method under edge equipment based on sparse anomaly perception

Abstract

The invention discloses an electric quantity prediction method under edge equipment based on sparse anomaly perception. The method comprises the following steps: S1, collecting electric quantity dataof K target buildings by edge equipment; S2, performing sparse anomaly marking on the abnormal data at the edge equipment end by adopting a sparse anomaly sensing method; S3, calculating a sparse abnormal discard probability and obtaining a total training set; S4, with a machine learning regression algorithm and a five-fold cross validation training model, randomly discarding abnormal data by utilizing a sparse abnormal discarding probability during each-fold cross validation and not participating in training; and S5, carrying out electric quantity prediction on to-be-predicted data by utilizing the machine learning model, and multiplying model prediction output by the sparse abnormal discarding probability to obtain final prediction. According to the method, a problem that a machine learning regression model based on mean square error loss is sensitive to abnormal data is solved; and meanwhile, the training data volume is reduced, the model training speed is increased, randomness is introduced, and the prediction precision and generalization ability of the model are improved.

Description

technical field [0001] The present invention relates to power forecasting in the energy field, in particular to a power forecasting method under edge devices based on sparse anomaly perception. Background technique [0002] Energy issues are closely related to human development. With the development of the times and the advancement of science and technology, electric energy has become one of the indispensable energy sources in human social life, and is the most important part of the energy field in modern society. demand is increasing day by day. As the basis of power system operation, optimization and control, electricity consumption forecasting is facing new challenges in today's environment of rapid development of energy systems. [0003] In recent years, with the rapid development of machine learning technology and deep learning technology, a large number of machine learning technology and deep learning technology have been applied to power forecasting, and good result...

AI Technical Summary

Problems solved by technology

And its biggest shortcoming is that it is easily affected by outliers. In response to this shortcoming, domestic and foreign scholars have made many improvements, most of which are aimed at the loss function itself, directly using the Huber loss function, etc.

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