Abstract: Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.
Turrado C.C et al. presented the missing data imputation method based on multivariate adaptive regression splines for handling missing data in electrical data loggers and showed that the proposed method outperformed the multivariate imputation by chained equations REF .
A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers
Sensors
Sensors
Medicine Engineering Computer Science