Forecasting of total daily solar energy generation using ARIMA: A case study

Sharif Atique, Subrina Noureen, Vishwajit Roy, Vinitha Subburaj, Stephen Bayne, Joshua MacFie

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

11 Scopus citations

Abstract

In this paper, a well known statistical modeling method named ARIMA has been used to forecast the total daily solar energy generated by a solar panel located in a research facility. The beauty of the ARIMA model lies in its simplicity and it can only be applied to stationary time series. So our time series data, which is seasonal and non-stationary, is transformed into a stationary one for applying the ARIMA model. The model is developed using sophisticated statistical techniques. The optimum model is chosen and validated using Akaike information criterion (AIC) and residual sum of squares (SSE). Error analysis is done to demonstrate the efficiency of the proposed method. The accuracy of the developed model can be further increased, which is subject to future research.

Original languageEnglish
Title of host publication2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages114-119
Number of pages6
ISBN (Electronic)9781728105543
DOIs
StatePublished - Mar 12 2019
Event9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019 - Las Vegas, United States
Duration: Jan 7 2019Jan 9 2019

Publication series

Name2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019

Conference

Conference9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019
CountryUnited States
CityLas Vegas
Period01/7/1901/9/19

Keywords

  • ARIMA
  • Forecasting
  • generation
  • solar
  • stationarity
  • time series

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