Method, device, and storage medium for predicting measurement error of electric energy meter

Abstract

The invention discloses a method, a device and a storage medium for predicting measurement errors of electric energy meters. The method includes: obtaining the historical temperature of the equipment where the electric energy meter is located in a cycle, and preprocessing the historical temperature to obtain a temperature-time curve; constructing a temperature drift correction factor according to the temperature-time curve, and establishing a temperature drift correction factor according to the temperature-time curve Improved ARIMA prediction model; combined with autocorrelation analysis and partial autocorrelation analysis to draw up the differential order, and based on the AIC criterion to draw up the autoregressive order, moving average order, temperature drift correction factor order, and complete the definition of the improved ARIMA forecasting model order; formulate the coefficients corresponding to the autoregressive order, moving average order, and temperature drift correction factor order, and input the difference data into the improved ARIMA prediction model currently obtained to obtain prediction error data. The invention can predict the measurement error of the electric energy meter through the improved ARIMA prediction model, and improve the prediction accuracy of the electric energy meter measurement error.

Description

Method, device and storage medium for measuring error prediction of electric energy meter technical field The present invention relates to the technical field of electric energy meter measurement, in particular to a kind of electric energy meter measurement error prediction method, device and storage media. Background technique [0002] The electric energy meter is a national legal electric energy measuring instrument, and the measurement inaccuracy will directly lead to electric energy trade disputes and hinder the electric energy. Therefore, it is very important to predict the measurement error of the electric energy meter and detect the inaccurate electric energy meter in time. The operation of the electric energy meter Line error prediction has become a research hotspot. [0003] The existing research "Analysis of the Operational Stability of Smart Electric Energy Meters in High Altitude Environments" shows that temperature is the main factor affecting the error of e...

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Problems solved by technology

This assumption will cause two problems: on the one hand, the actual operation of the electric energy meter is easily disturbed by long-term factors such as temperature difference between day and night and daily load fluctuations.

Moreover, the change cycle of such disturbances is much higher than the real-time error prediction interval, and it is difficult to make up for it by additional seasonal parameters

Therefore, it is difficult to reflect the long-term periodicity of interference factors when white noise and various noises are used as error sources; on the other hand, sporadic interference such as power grid transient faults and mechanical vibrations are prone to occur during the actual operation of electric energy meters

When the interference amplitude is much higher than the noise variance, the traditional seasonal ARIMA model established based on white noise or various noises is easy to get inaccurate autoregressive order and moving average order in the model order stage, which greatly reduces the Forecast accuracy

[0006] In summary, the traditional seasonal ARIMA model still has some room for improvement in the prediction of electric energy meter errors, and it is necessary to study how to extract the long-period temperature drift components contained in the signal, so as to improve the error caused by temperature drift of electric energy meters in a targeted manner. Long-period component interference, and how to use more detailed modeling, filtering, and weighting algorithms to reduce occasional interference caused by factors such as transient faults and mechanical vibrations

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