BiLSTM voltage deviation prediction method based on Bayesian optimization

Abstract

The invention discloses a BiLSTM voltage deviation prediction method based on Bayesian optimization, and the method comprises the steps of carrying out the standard deviation standardization processing of a voltage deviation time series data set, carrying out the data segmentation according to a proportion, and obtaining a training set and a verification set; training a BiLSTM voltage deviation prediction model by using the preprocessed voltage deviation data training set; inputting the verification set into a trained BiLSTM voltage deviation prediction model, obtaining a voltage deviation prediction value, then carrying out inverse standard deviation processing, taking a root-mean-square error as a target function for the hyper-parameter optimization of the BiLSTM voltage deviation prediction model, optimizing the hyper-parameters of the BiLSTM voltage deviation prediction model by using a Bayesian optimization algorithm, and obtaining an optimal hyper-parameter combination; and taking the optimal hyper-parameter combination as hyper-parameters of a BiLSTM prediction model, constructing a BiLSTM voltage deviation prediction model based on a Bayesian optimization algorithm, and predicting the voltage deviation time sequence data to obtain final prediction data. The invention is high in precision and reliable in prediction effect.

Description

technical field [0001] The invention relates to a BiLSTM voltage deviation prediction method based on Bayesian optimization, belonging to the fields of electrical engineering and voltage deviation. Background technique [0002] In recent years, the power industry has developed rapidly, and power users have higher and higher requirements for voltage deviation. The problem of voltage deviation will seriously damage the interests of power users and related power industries. Therefore, it is necessary to increase the monitoring of voltage deviation and obtain a large amount of voltage deviation data to provide a basis for in-depth analysis of voltage deviation trends. Effective analysis and prediction of voltage deviation data is helpful for staff to discover voltage deviation problems early and take corresponding measures to ensure the stable and safe operation of the power system. [0003] At present, there are few research results on the prediction of voltage deviation. Som...

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Problems solved by technology

[0004] The purpose of the present invention is to propose a BiLSTM voltage deviation prediction method based on Bayesian optimization to solve the problems of poor generalization ability and low prediction accuracy of the voltage deviation prediction model

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