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 contribution

1 Citation (Scopus)

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
Publication statusPublished - 2019 Apr
Event6th IEEE International Conference on Energy Smart Systems, ESS 2019 - Kyiv, Ukraine
Duration: 2019 Apr 172019 Apr 19

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
CountryUkraine
CityKyiv
Period19-04-1719-04-19

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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