A Comparison of Integrated Filtering and Prediction Methods for Smart Grids

Emmanuel Escobar-Avalos, Martín A. Rodríguez-Licea, Horacio Rostro-González, Allan G. Soriano-Sánchez and Francisco J. Pérez-Pinal
Additional contact information
Emmanuel Escobar-Avalos: Departamento de Electrónica, Instituto Tecnológico de Celaya, Antonio García Cubas 600, Celaya, Gto. C.P. 38010, Mexico
Martín A. Rodríguez-Licea: Departamento de Electrónica, CONACYT-Instituto Tecnológico de Celaya, Catedrático CONACYT, Antonio García Cubas 600, Celaya, Gto. C.P. 38010, Mexico
Horacio Rostro-González: Departamento de Electrónica, Universidad de Guanajuato, Carretera Salamanca–Valle de Santiago Km 3.5 + 1.6, Com. Palo Blanco, Salamanca, Gto. C.P. 36885, Mexico
Allan G. Soriano-Sánchez: Departamento de Electrónica, CONACYT-Instituto Tecnológico de Celaya, Catedrático CONACYT, Antonio García Cubas 600, Celaya, Gto. C.P. 38010, Mexico
Francisco J. Pérez-Pinal: Departamento de Electrónica, Instituto Tecnológico de Celaya, Antonio García Cubas 600, Celaya, Gto. C.P. 38010, Mexico

Energies, 2021, vol. 14, issue 7, 1-16

Abstract: The intelligent use of green and renewable energies requires reliable and preferably anticipated information regarding their availability and the behavior of meteorological variables in a scenario of natural intermittency. Examples of this are the smart grids, which can incorporate, among others, a charging system for electric vehicles and modern and predictive management techniques. However, some issues associated with such procedures are data captured by sensors and transducers with noise in their signals and low information repeatability under the same reading conditions. To tackle such problems, numerous filtering and data fitting techniques and various prediction methods have been developed, but an appropriate selection can be cumbersome. Also, some filtering techniques, such as RANdom SAmple Consensus (RANSAC) appear not to have been used in prediction scenarios for smart grids, to the authors’ knowledge. In this regard, this paper aims to present a comparison in terms of average error, determination coefficient, and cross validation that can be expected under prediction schemes as Multiple Linear Regression, Vector Support Machines and a Multilayer Perceptron Regression Neural Network (MLPRNN), with filtering/scaling methods such as Maximum and Minimum, L2 Norm, Standard Scale, and RANSAC. Cross validation allows to flag problems like overfitting or selection bias, and this comparison is another novelty for smart grid scenarios, to the authors’ knowledge. Although many combinations were analyzed, RANSAC, with L2 Norm filtering and an MLPRNN for prediction, generate the best results. RANSAC algorithm with L2 Norm is a novelty for filtering and predicting in smart grids, and through an MLPRNN, the R 2 error can be reduced to 0.8843, the MSE to 0.8960, and the cross validation accuracy can be increased to 0.44 (±0.2).

Keywords: weather forecast; smart grid; RANSAC; vector support machines; multilayer perceptron neural network; multiple linear regression; cross validation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/7/1980/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/7/1980/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:7:p:1980-:d:529462

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().