A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers.

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ID: 21855
2016
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Abstract
The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms.
Reference Key
turrado2016asensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Turrado, Concepción Crespo;Sánchez Lasheras, Fernando;Calvo-Rollé, José Luis;Piñón-Pazos, Andrés-José;Melero, Manuel G;de Cos Juez, Francisco Javier;
Journal sensors
Year 2016
DOI
10.3390/s16091467
URL
Keywords Keywords not found

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