Analysis and application of seasonal ARIMA model in Energy Demand Forecasting: A case study of small scale agricultural load

Subrina Noureen, Sharif Atique, Vishwajit Roy, Stephen Bayne

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

7 Scopus citations

Abstract

This paper has presented the use of Auto Regressive Integrated Moving Average (ARIMA) method for forecasting of seasonal time series data. The dataset that has been used for modeling and forecasting is a small-scale agricultural load. ARIMA method can be applied only when the time series data is stationary. As seasonal variations make a time series non-stationary, this paper also presents analyses on testing stationarity and transforming non-stationarity into stationarity. Lastly, model has been developed with a specific selection of orders for autoregressive terms, moving average terms, differencing and seasonality and the forecasting performance has been tested and compared with the actual value. The results are encouraging, however there is scope of further research in refining the idea.

Original languageEnglish
Title of host publication2019 IEEE 62nd International Midwest Symposium on Circuits and Systems, MWSCAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages521-524
Number of pages4
ISBN (Electronic)9781728127880
DOIs
StatePublished - Aug 2019
Event62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019 - Dallas, United States
Duration: Aug 4 2019Aug 7 2019

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2019-August
ISSN (Print)1548-3746

Conference

Conference62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019
Country/TerritoryUnited States
CityDallas
Period08/4/1908/7/19

Keywords

  • ARIMA model
  • Agricultural Load
  • Load Forecasting
  • Seasonal ARIMA

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