Energy demand forecasting and error correction with decision tree

Marla De Guadalupe Cota Ortiz, Pedro Flores Perez

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Search solutions to optimize resources for energy demand is a complex problem. Factors such as the increase in energy consumption and environmental variation are basic to estimate the precision of the resource that will be generate. This article describes the procedure to correct errors on the results of energy demand forecasts, previously obtained with a library based on time series and the application of the 2G algorithm in the error correction stage. The experimental results indicate efficiency, however, for optimal results, it is necessary to have a larger dataset.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas431-435
Número de páginas5
ISBN (versión digital)9781728176246
DOI
EstadoPublicada - dic 2020
Evento2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, Estados Unidos
Duración: 16 dic 202018 dic 2020

Serie de la publicación

NombreProceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conferencia

Conferencia2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
País/TerritorioEstados Unidos
CiudadLas Vegas
Período16/12/2018/12/20

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© 2020 IEEE.

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