Smart meter failure prediction based on weakened grey markov model with IOWA operator

Abstract

Scientifically predicting the annual failure quantity of smart meters is of significant importance for enhancing the economic benefits of smart meters and promoting the stable operation of smart grids. In this paper, traditional Grey Markov prediction models and Grey Markov models with weakened buffering operators are employed to predict smart meter failure data. To improve prediction accuracy, an Induced Ordered Weighted Averaging (IOWA) operator is introduced to construct a combination prediction model. Based on this approach, we predict the annual failure quantity of smart meters for a certain company in Wuhan, China, from 2020 to 2022 using data from 2012 to 2019. Accuracy indicators, such as correlation degree (G) and average relative error (P), have improved from level three to level two, indicating that the combination prediction model based on the IOWA operator effectively enhances prediction accuracy. This method demonstrates the feasibility and effectiveness of predicting smart meter failures.