A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State

K. W. Yu, C. H. Hsu, S. M. Yang

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

9 Scopus citations

Abstract

This paper proposes a model having both linear and nonlinear system dynamics by integrating both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model to simulate electrical energy supply inherent with strong seasonal and periodic characteristics in power system. Accurate electrical load forecast becomes possible by the integrated model for the ARIMA is effective to electricity load time series inherent with seasonal fluctuations as well as strong 7-day (per week) periodic characteristics. By using the input of historical daily electricity load data, weather data, and holiday effect variables, the integrated model is shown to be more accurate than the ANN model, the ARIMA model, the classical ARIMA-ANN model, and other well-known methods in the prediction and the forecast of electrical load in normal summer week, normal winter week, 3/4-day holiday week, long holiday week, and extreme weather week.

Original languageEnglish
Title of host publication2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-24
Number of pages6
ISBN (Electronic)9781728121598
DOIs
StatePublished - 04 2019
Externally publishedYes
Event6th IEEE International Conference on Energy Smart Systems, ESS 2019 - Kyiv, Ukraine
Duration: 17 04 201919 04 2019

Publication series

Name2019 IEEE 6th International Conference on Energy Smart Systems, ESS 2019 - Proceedings

Conference

Conference6th IEEE International Conference on Energy Smart Systems, ESS 2019
Country/TerritoryUkraine
CityKyiv
Period17/04/1919/04/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Electrical load dynamics
  • Short-term load forecasting
  • Time series

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