Citation

Crespo Turrado C, Sánchez Lasheras F, Calvo-Rollé JL, Piñón-Pazos AJ, de Cos Juez FJ. Sensors (Basel) 2015; 15(12): 31069-31082.

Affiliation

Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo 33004, Spain. fjcos@uniovi.es.

Copyright

(Copyright © 2015, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s151229842

PMID

26690437

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.

Language: en