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• W4200606445 abstract "Abstract The unavoidable outliers and the characteristics of the small sample dataset affect the performance of the failure rate (FR) prediction and reliability analysis model of smart meters (SMs). To solve these problems, we choose the basic error (BE) as the performance index of the equipment and propose a reliability evaluation framework for SMs by combining adaptive Gauss genetic-algorithm-autoregressive integrated moving average (AGG-ARIMA) and proportional FR (PFR) for the first time. First, the ARIMA model is used to predict the BEs to describe the performance of SMs. Then, an AGG is used to optimize the order of ARIMA and a PFR model is established to analyze the reliability of batch SMs through the BE predictions. Finally, actual datasets from four companies are used to verify the effectiveness of our evaluation framework. The experimental results show that our framework has better reliability assessment performance for SMs under small sample conditions, and has strong adaptability to the analysis of individual meters." @default.
• W4200606445 created "2021-12-31" @default.
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• W4200606445 date "2022-01-11" @default.
• W4200606445 modified "2023-10-18" @default.
• W4200606445 title "A reliability evaluation framework for smart meters based on AGG-ARIMA and PFR" @default.
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• W4200606445 doi "https://doi.org/10.1088/1361-6501/ac42e6" @default.
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