A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Sensors

Concepción Crespo Turrado⁴  Fernando Sánchez Lasheras³  José Luis Calvo-Rollé¹  Andrés José Piñón-Pazos¹  Francisco Javier de Cos Juez² 

[1] Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain;Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo 33004, Spain;;Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, Gijón 33204, SpainMaintenance Department, University of Oviedo, San Francisco 3, Oviedo 33007, Spain;

关键词: missing data imputation;    multivariate imputation by chained equations (MICE);    Multivariate adaptive regression splines (MARS);    quality of electric supply;    voltage;    current;    power factor;   

DOI  :  10.3390/s151229842

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.