TY  - JOUR
PY  - 2015//
TI  - A new missing data imputation algorithm applied to electrical data loggers
JO  - Sensors (Basel)
A1  - Crespo Turrado, Concepción
A1  - Sánchez Lasheras, Fernando
A1  - Calvo-Rollé, José Luis
A1  - Piñón-Pazos, Andrés José
A1  - de Cos Juez, Francisco Javier
SP  - 31069
EP  - 31082
VL  - 15
IS  - 12
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s151229842
ID  - ref1
ER  -