Crespo Turrado, ConcepciónSánchez Lasheras, FernandoCalvo-Rolle, José LuisPiñón-Pazos, A.Cos Juez, Francisco Javier de2017-09-152017-09-152015Crespo Turrado, C., Sánchez Lasheras, F., Calvo-Rolle, J. L., Piñón-Pazos, A. J., & de Cos Juez, F. J. (2015). A new missing data imputation algorithm applied to electrical data loggers. Sensors, 15(12), 31069-31082.1424-8220http://hdl.handle.net/2183/19477Nowadays, 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.engReconocimiento 3.0http://creativecommons.org/licenses/by/3.0/Missing data imputationMultivariate imputation by chained equations (mice)Multivariate adaptive regression splines (mars)Quality of electric supplyVoltageCurrentPower factorjournal articleopen access