A forecasting method for metering error of electric energy based on intrinsic time-scale decomposition and time series analysis

Error forecasting for electric energy measurement equipment is the foundation of its operation condition forecasting and fault early warning. This paper proposes a kind of forecasting model and algorithm for electric energy metering error based on intrinsic time-scale decomposition (ITD) and time series analysis. Error is decomposed into a steady baseline component and multiple rotation components of time series by ITD method. This method adapts subsection linearization between adjacent extreme points and can significantly improve the decomposition efficiency for each error component. Considering the stationary and non-stationary characteristics of trend and rotation component respectively, auto regressive moving average (ARMA) and auto regressive moving integrated average (ARIMA) are used to establish the forecasting model for measurement error trend and rotation components. Then the forecasting value will be acquired by adding each error forecasting component. The effectiveness of the proposed method is verified by the analysis of metering error of electric energy monitoring data in a 110 kV substation.