Electric energy quality prediction method based on similar days and improved LSTM

Abstract

The invention relates to an electric energy quality prediction method based on similar days and improved LSTM. The method comprises the following steps: 1) collecting electric energy quality steady-state index data of a certain monitoring point in a certain region within a period of time and meteorological data of the region; 2) performing feature dimension reduction on the meteorological data byadopting a kernel principal component analysis method to obtain similar day feature vectors; 3) calculating meteorological factor matching coefficients of the historical day and the to-be-predicted day by adopting a grey correlation algorithm, and determining a similar day set; 4) selecting power quality historical data similar to the day to be predicted and similar day set data as a training sample set of the LSTM neural network, and optimizing LSTM neural network parameters by adopting a method of combining a Dropout algorithm and an Adam adaptive learning rate optimization algorithm, and taking the similar day feature vector as a model input variable to obtain a prediction result of the electric energy quality of each moment of the to-be-predicted day. Compared with the prior art, the method has the advantages of avoiding training overfitting, avoiding information interference and information repetition, improving prediction accuracy and the like.

Description

technical field [0001] The invention relates to the technical field of power quality, in particular to a method for predicting power quality based on similar days and improved LSTM. Background technique [0002] In recent years, with the continuous development of smart grids, the basic and operational data in the field of power distribution has experienced explosive growth, which contains a large amount of valuable information. Traditional data analysis methods cannot adapt to the characteristic requirements of multi-source and heterogeneous power quality data. Power grids in various regions have continuously upgraded their power quality monitoring systems, accumulating massive amounts of power quality data. It is an important premise to ensure high-quality and reliable power supply of the power system by digging deeply into the internal connection and changing rules of these data, scientifically predicting its changing trend, and timely warning of the power quality that aff...

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Problems solved by technology

Due to the characteristics of periodic, random, and nonlinear changes in power quality, it is difficult to achieve nonlinear data fitting using linear regression or time series methods; the gray forecasting method requires that the discreteness of the data should be as small as possible; To a certain extent, the prediction error of a single model can be reduced, but due to the different status of power quality monitoring data, in some cases, there will be a phenomenon that the prediction effect of a single prediction model is better than that of a combined model, and the calculation of the combined model is complex and costly. long time

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