Aiming at the demand for scientific periodic replace of smart meters, a remaining useful life (RUL) prediction method based on the basic error data of smart meters is established. Firstly, the Person correlation coefficient is adopted to screen out the environmental stress that has great impact on the basic error data of the smart meter as the model input; then the Gaussian kernel, the Matern32 kernel, and the periodic kernel are adopted to match the basic error trend of the smart meter under the multi-stress environment; the Bayesian method and the Monte Carlo Markov Chain (MCMC) are used to solve the model. Experiment results show that smart meters from different companies have different environmental tolerances. Under typical environmental condition of high dry heat, the posterior upper quartile value of the smart meters from A company reaches the threshold, and the RUL is 43 months; the smart meters from company B has no general failures happened, however there will be a great possibility to failure in the next 47 months, and troubleshooting and error verification should be started. In the typical environment of high dry heat, the accelerated out-of-tolerance failure phenomenon of smart meters does not meet the 8-year verification period stipulated in the measurement regulations, and the periodic verification work should be dynamically adjusted.