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Smart meter is a typical edge device that measures and records the energy data. The usage of smart meter data can improve the cyber physical relationship between the smart grid and cyber physical system. Millions of smart meters have been installed all around the world and the malfunction detection of large volume meter is a big issue. On site checking is a costing work and cannot meet the requirement for large scale meters. Online malfunctional meter detection and verification based on meter data analytics is a solution to the meter detection problem. For the purpose of detecting malfunctional smart meter, the low-voltage energy system model is studied and a meter error estimation method is proposed in this paper. This method adopts a decision tree to filter the abnormal data and classify data with different energy loss levels. Then clustering the data to obtain the data set with different energy usage behavior. A meter data matrix is constructed and meter error can be calculated from the solution of the matrix equation. A Recursive algorithm is adopted to solve the equation and estimate the meter error. The meter error above the regulation threshold will be classified as a malfunctional meter. The proposed approach has achieved higher accuracy in the experiment.

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Smart meter has been considered as the key element in the smart grid and current smart meter verification method failed to coverage all deployed meters. Online calibration for is a novel approach which calculate meter error by analysing meter reading data. This paper presents a comprehensive survey and basic model of smart meter online calibration and proposed a recursive algorithm to estimate meter error. Concrete steps have been illustrated and testing case shows the higher accuracy of the method.  Finally, some issues of online calibration in application were discussed.