A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers
Ver/Abrir
Use este enlace para citar
http://hdl.handle.net/2183/19477Colecciones
- GI-CTC - Artigos [83]
Metadatos
Mostrar el registro completo del ítemTítulo
A New Missing Data Imputation Algorithm Applied to Electrical Data LoggersAutor(es)
Fecha
2015Cita bibliográfica
Crespo 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.
Resumen
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.
Palabras clave
Missing data imputation
Multivariate imputation by chained equations (mice)
Multivariate adaptive regression splines (mars)
Quality of electric supply
Voltage
Current
Power factor
Multivariate imputation by chained equations (mice)
Multivariate adaptive regression splines (mars)
Quality of electric supply
Voltage
Current
Power factor
Versión del editor
Derechos
Reconocimiento 3.0
ISSN
1424-8220