Energy demand forecasting and error correction with decision tree

Marla De Guadalupe Cota Ortiz; Pedro Flores Perez

doi:10.1109/CSCI51800.2020.00080

Energy demand forecasting and error correction with decision tree

Marla De Guadalupe Cota Ortiz, Pedro Flores Perez

Departamento de Matemáticas

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión 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 original	Inglés
Título de la publicación alojada	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	431-435
Número de páginas	5
ISBN (versión digital)	9781728176246
DOI	https://doi.org/10.1109/CSCI51800.2020.00080
Estado	Publicada - dic. 2020
Evento	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, Estados Unidos Duración: 16 dic. 2020 → 18 dic. 2020

Serie de la publicación

Nombre	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

Conferencia

Conferencia	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
País/Territorio	Estados Unidos
Ciudad	Las Vegas
Período	16/12/20 → 18/12/20

Nota bibliográfica

Publisher Copyright:
© 2020 IEEE.

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10.1109/CSCI51800.2020.00080

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Guadalupe Cota Ortiz, M. D., & Flores Perez, P. (2020). Energy demand forecasting and error correction with decision tree. En Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 (pp. 431-435). Artículo 9458068 (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI51800.2020.00080

Guadalupe Cota Ortiz, Marla De ; Flores Perez, Pedro. / Energy demand forecasting and error correction with decision tree. Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 431-435 (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020).

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title = "Energy demand forecasting and error correction with decision tree",

abstract = "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.",

keywords = "classification, energy, error correction, forecasting",

author = "{Guadalupe Cota Ortiz}, {Marla De} and {Flores Perez}, Pedro",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 ; Conference date: 16-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

doi = "10.1109/CSCI51800.2020.00080",

language = "Ingl{\'e}s",

series = "Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020",

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Guadalupe Cota Ortiz, MD & Flores Perez, P 2020, Energy demand forecasting and error correction with decision tree. En Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020., 9458068, Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 431-435, 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, Las Vegas, Estados Unidos, 16/12/20. https://doi.org/10.1109/CSCI51800.2020.00080

Energy demand forecasting and error correction with decision tree. / Guadalupe Cota Ortiz, Marla De; Flores Perez, Pedro.
Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 431-435 9458068 (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Guadalupe Cota Ortiz, Marla De

AU - Flores Perez, Pedro

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

KW - classification

KW - energy

KW - error correction

KW - forecasting

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M3 - Contribución a la conferencia

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BT - Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Guadalupe Cota Ortiz MD, Flores Perez P. Energy demand forecasting and error correction with decision tree. En Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 431-435. 9458068. (Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020). doi: 10.1109/CSCI51800.2020.00080

Energy demand forecasting and error correction with decision tree

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