Forecasting Building Electric Consumption Patterns Through Statistical Methods

Xavier Serrano-Guerrero; Luis Fernando Siavichay; Jean Michel Clairand; Guillermo Escrivá-Escrivá

doi:10.1007/978-3-030-32033-1_16

Forecasting Building Electric Consumption Patterns Through Statistical Methods

Xavier Serrano-Guerrero, Luis Fernando Siavichay, Jean Michel Clairand, Guillermo Escrivá-Escrivá

Energy Research Group (GIE)

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

4 Scopus citations

Abstract

The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).

Original language	English
Title of host publication	Advances in Emerging Trends and Technologies - Volume 2
Editors	Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
Publisher	Springer
Pages	164-175
Number of pages	12
ISBN (Print)	9783030320324
DOIs	https://doi.org/10.1007/978-3-030-32033-1_16
State	Published - 1 Jan 2020
Event	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador Duration: 29 May 2019 → 31 May 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1067
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Country/Territory	Ecuador
City	quito
Period	29/05/19 → 31/05/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

ARIMA
Electric demand
Forecast
Load Uncertainties
Statistical methods
Winter

Access to Document

10.1007/978-3-030-32033-1_16

Cite this

Serrano-Guerrero, X., Siavichay, L. F., Clairand, J. M., & Escrivá-Escrivá, G. (2020). Forecasting Building Electric Consumption Patterns Through Statistical Methods. In M. Botto-Tobar, J. León-Acurio, A. Díaz Cadena, & P. Montiel Díaz (Eds.), Advances in Emerging Trends and Technologies - Volume 2 (pp. 164-175). (Advances in Intelligent Systems and Computing; Vol. 1067). Springer. https://doi.org/10.1007/978-3-030-32033-1_16

Serrano-Guerrero, Xavier ; Siavichay, Luis Fernando ; Clairand, Jean Michel et al. / Forecasting Building Electric Consumption Patterns Through Statistical Methods. Advances in Emerging Trends and Technologies - Volume 2. editor / Miguel Botto-Tobar ; Joffre León-Acurio ; Angela Díaz Cadena ; Práxedes Montiel Díaz. Springer, 2020. pp. 164-175 (Advances in Intelligent Systems and Computing).

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title = "Forecasting Building Electric Consumption Patterns Through Statistical Methods",

abstract = "The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).",

keywords = "ARIMA, Electric demand, Forecast, Load Uncertainties, Statistical methods, Winter",

author = "Xavier Serrano-Guerrero and Siavichay, {Luis Fernando} and Clairand, {Jean Michel} and Guillermo Escriv{\'a}-Escriv{\'a}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 ; Conference date: 29-05-2019 Through 31-05-2019",

year = "2020",

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Serrano-Guerrero, X, Siavichay, LF, Clairand, JM & Escrivá-Escrivá, G 2020, Forecasting Building Electric Consumption Patterns Through Statistical Methods. in M Botto-Tobar, J León-Acurio, A Díaz Cadena & P Montiel Díaz (eds), Advances in Emerging Trends and Technologies - Volume 2. Advances in Intelligent Systems and Computing, vol. 1067, Springer, pp. 164-175, 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019, quito, Ecuador, 29/05/19. https://doi.org/10.1007/978-3-030-32033-1_16

Forecasting Building Electric Consumption Patterns Through Statistical Methods. / Serrano-Guerrero, Xavier; Siavichay, Luis Fernando; Clairand, Jean Michel et al.
Advances in Emerging Trends and Technologies - Volume 2. ed. / Miguel Botto-Tobar; Joffre León-Acurio; Angela Díaz Cadena; Práxedes Montiel Díaz. Springer, 2020. p. 164-175 (Advances in Intelligent Systems and Computing; Vol. 1067).

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

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AB - The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).

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Serrano-Guerrero X, Siavichay LF, Clairand JM, Escrivá-Escrivá G. Forecasting Building Electric Consumption Patterns Through Statistical Methods. In Botto-Tobar M, León-Acurio J, Díaz Cadena A, Montiel Díaz P, editors, Advances in Emerging Trends and Technologies - Volume 2. Springer. 2020. p. 164-175. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-32033-1_16

Forecasting Building Electric Consumption Patterns Through Statistical Methods

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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