Electric energy meter metering error prediction method and device and storage medium

Abstract

The invention discloses an electric energy meter metering error prediction method and device and a storage medium. The method comprises the steps that the historical temperature of equipment where an electric energy meter is located in a period is acquired, the historical temperature is preprocessed, and a temperature-time curve is obtained; constructing a temperature excursion correction factor according to the temperature-time curve, and establishing an improved ARIMA prediction model according to the temperature excursion correction factor; drawing up a difference order in combination with autocorrelation analysis and partial autocorrelation analysis, drawing up an autoregression order, a moving average order and a temperature drift correction factor order based on an AIC criterion, and completing order determination of the improved ARIMA prediction model; and formulating coefficients corresponding to an autoregression order, a moving average order and a temperature drift correction factor order, and inputting differential data into the currently obtained improved ARIMA prediction model to obtain prediction error data. According to the method, the metering error of the electric energy meter can be predicted through the improved ARIMA prediction model, and the metering error prediction accuracy of the electric energy meter is improved.

Description

technical field [0001] The invention relates to the technical field of metering of electric energy meters, in particular to a method, device and storage medium for predicting metering errors of electric energy meters. Background technique [0002] The electric energy meter is a national statutory electric energy measuring instrument. Measurement inaccuracy will directly lead to electric energy trade disputes and hinder the normal operation of the electricity market. Therefore, it is very important to predict the measurement error of the electric energy meter and find out the inaccurate electric energy meter in time. become a research hotspot. [0003] The existing research "Analysis of the Stability of Smart Energy Meter Operation in High Altitude Environment" shows that temperature is the most important factor affecting the error of the energy meter, and its influence is far higher than that of humidity, radiation intensity, and higher than the load current. Therefore, the...

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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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