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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	431-435
Number of pages	5
ISBN (Electronic)	9781728176246
DOIs	https://doi.org/10.1109/CSCI51800.2020.00080
State	Published - Dec 2020
Event	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 - Las Vegas, United States Duration: 16 Dec 2020 → 18 Dec 2020

Publication series

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

Conference

Conference	2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020
Country/Territory	United States
City	Las Vegas
Period	16/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

classification
energy
error correction
forecasting

Access to Document

10.1109/CSCI51800.2020.00080

Cite this

Guadalupe Cota Ortiz, M. D., & Flores Perez, P. (2020). Energy demand forecasting and error correction with decision tree. In Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 (pp. 431-435). Article 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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pages = "431--435",

booktitle = "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. in 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, United States, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Guadalupe Cota Ortiz, Marla De

AU - Flores Perez, Pedro

PY - 2020/12

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

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ER -

Guadalupe Cota Ortiz MD, Flores Perez P. Energy demand forecasting and error correction with decision tree. In 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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