Abstract
The measurement error of smart meters will change after long-term operation. Using IoT devices and big data technology for remote error monitoring is an important development direction for smart meter verification. However, missing data leads to a lack of sufficient available samples for smart meter error estimates. In this paper, a remote estimation method for smart meter error considering high-precision missing data repair is proposed. Missing data is reconstructed using advanced ensemble learning methods to increase the number of available samples. Considering that the randomness of samples increases with the number of available samples, an improved energy model based on sequential weighting is proposed to improve the stability of the model. Finally, a multivariate linear equation system is established and solved by the least square method. Taking the measured data of a certain transformer area in China as an example for simulation, the results show that the proposed method can achieve high-precision reconstruction, and the reconstructed data is feasible and effective in the improved error estimation model.
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