A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers
AbstractNowadays, 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. View Full-Text
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Crespo Turrado, C.; Sánchez Lasheras, F.; Calvo-Rollé, J.L.; Piñón-Pazos, A.J.; de Cos Juez, F.J. A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers. Sensors 2015, 15, 31069-31082.
Crespo Turrado C, Sánchez Lasheras F, Calvo-Rollé JL, Piñón-Pazos AJ, de Cos Juez FJ. A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers. Sensors. 2015; 15(12):31069-31082.Chicago/Turabian Style
Crespo Turrado, Concepción; Sánchez Lasheras, Fernando; Calvo-Rollé, José L.; Piñón-Pazos, Andrés J.; de Cos Juez, Francisco J. 2015. "A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers." Sensors 15, no. 12: 31069-31082.