Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling-Reference-Cited by-同舟云学术

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

Published:2020-06-20 Issue:12 Volume:10 Page:4239
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Elias Israel¹,Rubio José de Jesús¹^ORCID,Martinez Dany Ivan¹,Vargas Tomas Miguel¹,Garcia Victor¹,Mujica-Vargas Dante²^ORCID,Meda-Campaña Jesus Alberto³^ORCID,Pacheco Jaime¹^ORCID,Gutierrez Guadalupe Juliana¹,Zacarias Alejandro¹

Affiliation:

1. Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, Ciudad de México 02250, Mexico

2. Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca-Morelos 62490, Mexico

3. Sección de Estudios de Posgrado e Investigación, ESIME Zacatenco, Instituto Politécnico Nacional, Av. IPN S/N, Col. Lindavista, Ciudad de México 07738, Mexico

Abstract

The modified backpropagation algorithm based on the backpropagation with momentum is used for the parameters updating of a radial basis mapping (RBM) network, where it requires of the best hyper-parameters for more precise modeling. Seeking of the best hyper-parameters in a model it is not an easy task. In this article, a genetic algorithm is used to seek of the best hyper-parameters in the modified backpropagation for the parameters updating of a RBM network, and this RBM network is used for more precise electricity consumption modeling in a city. The suggested approach is called genetic algorithm with a RBM network. Additionally, since the genetic algorithm with a RBM network starts from the modified backpropagation, we compare both approaches for the electricity consumption modeling in a city.

Publisher

MDPI AG

Link

https://www.mdpi.com/2076-3417/10/12/4239/pdf

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