A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers
| Autor: | Concepción Crespo Turrado, Fernando Sánchez Lasheras, José Luis Calvo-Rollé, Andrés-José Piñón-Pazos, Manuel G. Melero, Francisco Javier de Cos Juez |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
missing data imputation
multivariate imputation by chained equations (MICE) Mahalanobis distances Self-Organized Maps Neural Networks (SOM) Adaptive Assignation Algorithm (AAA) Multivariate Adaptive Regression Splines (MARS) quality of electric supply voltage current power factor Chemical technology TP1-1185 |
| Zdroj: | Sensors, Vol 16, Iss 9, p 1467 (2016) |
| Druh dokumentu: | article |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s16091467 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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