Forecasting Building Electric Consumption Patterns Through Statistical Methods

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

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

2 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 originalInglés
Título de la publicación alojadaAdvances in Emerging Trends and Technologies - Volume 2
EditoresMiguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
EditorialSpringer
Páginas164-175
Número de páginas12
ISBN (versión impresa)9783030320324
DOI
EstadoPublicada - 1 ene 2020
Evento1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador
Duración: 29 may 201931 may 2019

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
Volumen1067
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365

Conferencia

Conferencia1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
País/TerritorioEcuador
Ciudadquito
Período29/05/1931/05/19

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