Forecasting Day-Ahead Electricity Prices With A SARIMAX Model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electricity prices display nonlinear behaviour making it difficult to forecast prices in the market. In addition, various external factors influence electricity prices therefore predicting the day-ahead electricity price is subject to other factors fluctuating. Time-series models learn to follow past market trends and then use historical information as training input to predict future output. This paper focusses on understanding and interpreting statistical approaches for electricity price forecasting and explains these techniques through time-series application with real energy data. The model considered here is a Seasonal AutoRegressive Integrated Moving Average model with eXogenous variables (SARIMAX) as electricity prices follow a seasonal pattern controlled by various external factors. By applying algorithm rules for differencing to remove continuing trends, the data becomes stationary and parameters, 14 external factors, are chosen to predict day ahead electricity prices. In the presented experimental results, the Root Mean Square Error (RMSE) was reasonably low and the model accurately predicted electricity prices.

Original languageEnglish
Title of host publicationThe 2019 IEEE Symposium Series on Computational Intelligence
Place of PublicationXiamen, China
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1523-1529
Number of pages7
ISBN (Electronic)9781728124858
ISBN (Print)978-1-7281-2486-5
DOIs
Publication statusPublished - 20 Feb 2020
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
CountryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • Electricity price forecasting
  • SARIMAX modelling
  • Short-term
  • Seasonality
  • Exogenous variables

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  • Cite this

    Mc Hugh, C., Coleman, S., Kerr, D., & McGlynn, D. (2020). Forecasting Day-Ahead Electricity Prices With A SARIMAX Model. In The 2019 IEEE Symposium Series on Computational Intelligence (pp. 1523-1529). [9002930] (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI44817.2019.9002930