Forecasting Building Electric Consumption Patterns Through Statistical Methods

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 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 languageEnglish
Title of host publicationAdvances in Emerging Trends and Technologies - Volume 2
EditorsMiguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
PublisherSpringer
Pages164-175
Number of pages12
ISBN (Print)9783030320324
DOIs
StatePublished - 1 Jan 2020
Event1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador
Duration: 29 May 201931 May 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1067
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Country/TerritoryEcuador
Cityquito
Period29/05/1931/05/19

Keywords

  • ARIMA
  • Electric demand
  • Forecast
  • Load Uncertainties
  • Statistical methods
  • Winter

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