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

Grupo de Investigación en Energías (GIE)

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

4 Citas (Scopus)

Resumen

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).

Idioma original	Inglés
Título de la publicación alojada	Advances in Emerging Trends and Technologies - Volume 2
Editores	Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
Editorial	Springer
Páginas	164-175
Número de páginas	12
ISBN (versión impresa)	9783030320324
DOI	https://doi.org/10.1007/978-3-030-32033-1_16
Estado	Publicada - 1 ene. 2020
Evento	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador Duración: 29 may. 2019 → 31 may. 2019

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1067
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
País/Territorio	Ecuador
Ciudad	quito
Período	29/05/19 → 31/05/19

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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

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Serrano-Guerrero, X., Siavichay, L. F., Clairand, J. M., & Escrivá-Escrivá, G. (2020). Forecasting Building Electric Consumption Patterns Through Statistical Methods. En 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",

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Serrano-Guerrero, X, Siavichay, LF, Clairand, JM & Escrivá-Escrivá, G 2020, Forecasting Building Electric Consumption Patterns Through Statistical Methods. En 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).

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

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T1 - Forecasting Building Electric Consumption Patterns Through Statistical Methods

AU - Serrano-Guerrero, Xavier

AU - Siavichay, Luis Fernando

AU - Clairand, Jean Michel

AU - Escrivá-Escrivá, Guillermo

PY - 2020/1/1

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N2 - 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).

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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KW - Electric demand

KW - Forecast

KW - Load Uncertainties

KW - Statistical methods

KW - Winter

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Serrano-Guerrero X, Siavichay LF, Clairand JM, Escrivá-Escrivá G. Forecasting Building Electric Consumption Patterns Through Statistical Methods. En Botto-Tobar M, León-Acurio J, Díaz Cadena A, Montiel Díaz P, editores, 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

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Conferencia

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