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

• Main Authors: Israel Elias, José de Jesús Rubio, Dany Ivan Martinez, Tomas Miguel Vargas, Victor Garcia, Dante Mujica-Vargas, Jesus Alberto Meda-Campaña, Jaime Pacheco, Guadalupe Juliana Gutierrez, Alejandro Zacarias
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Applied Sciences
• Subjects: genetic algorithm; backpropagation; RBM network; electricity consumption
• Online Access: https://www.mdpi.com/2076-3417/10/12/4239

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.