Time series forecasting of total daily solar energy generation: A comparative analysis between ARIMA and machine learning techniques

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

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

Abstract

In this paper, the potential of machine learning based methods for time series forecasting of total daily solar energy generation has been explored. Firstly, the time series is modeled using the seasonal version of well known classical method auto regressive integrated moving average (ARIMA) and its performance is later compared to two other popular machine learning methods, support vector machine (SVM) and artificial neural network (ANN). The potential of machine learning based methods in this line of work is demonstrated by the superior performance of SVM. However, the reasons behind the low yield of ANN need to be inspected to enhance our understanding. In spite of SVM's relative success in prediction of solar generation, the overall accuracy still needs to be improved and the methods to achieve this objective should be researched in future.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Green Technologies Conference, GreenTech 2020
EditorsPierre F. Tiako, Robert Scolli, Tom Jobe
PublisherIEEE Computer Society
Pages175-180
Number of pages6
ISBN (Electronic)9781728150178
DOIs
StatePublished - Apr 1 2020
Event2020 IEEE Green Technologies Conference, GreenTech 2020 - Virtual, Oklahoma City, United States
Duration: Apr 1 2020Apr 3 2020

Publication series

NameIEEE Green Technologies Conference
Volume2020-April
ISSN (Electronic)2166-5478

Conference

Conference2020 IEEE Green Technologies Conference, GreenTech 2020
Country/TerritoryUnited States
CityVirtual, Oklahoma City
Period04/1/2004/3/20

Keywords

  • ANN
  • ARIMA
  • Forecasting
  • SVM
  • solar
  • time series

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