Hybrid algorithm for missing data imputation and its application to electrical data loggers
| Autor: | Francisco Javier de Cos Juez, Fernando Las-Heras, Manuel G. Melero, José Luis Calvo-Rolle, Concepción Crespo Turrado, Andrés-José Piñón-Pazos |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Multivariate Adaptive Regression Splines (MARS)
quality of electric supply Engineering Multivariate statistics Current 02 engineering and technology Power factor lcsh:Chemical technology computer.software_genre 01 natural sciences Biochemistry Article Analytical Chemistry missing data imputation Multivariate adaptive regression splines (mars) Quality of electric supply Data logger Mahalanobis distances 0202 electrical engineering electronic engineering information engineering multivariate imputation by chained equations (MICE) Self-Organized Maps Neural Networks (SOM) Adaptive Assignation Algorithm (AAA) voltage current power factor lcsh:TP1-1185 Imputation (statistics) Electrical and Electronic Engineering Instrumentation Mahalanobis distance Artificial neural network business.industry 010401 analytical chemistry Missing data imputation Voltage Missing data Atomic and Molecular Physics and Optics 0104 chemical sciences Adaptive assignation algorithm (aaa) Harmonics 020201 artificial intelligence & image processing Data mining business Multivariate imputation by chained equations (MICE) computer |
| Zdroj: | RUO. Repositorio Institucional de la Universidad de Oviedo instname RUC. Repositorio da Universidade da Coruña Sensors; Volume 16; Issue 9; Pages: 1467 Sensors (Basel, Switzerland) Sensors, Vol 16, Iss 9, p 1467 (2016) |
| ISSN: | 2014-5764 |
| 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 Francisco Javier de Cos Juez and Fernando Sánchez Lasheras appreciate support from the Spanish Economics and Competitiveness Ministry, through grant AYA2014-57648-P and the Government of the Principality of Asturias (Consejería de Economía y Empleo), through grant FC-15-GRUPIN14-017 |
| Databáze: | OpenAIRE |
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