Seasonal Models for Forecasting Day-Ahead Electricity Prices

Catherine Mc Hugh, Sonya Coleman, Dermot Kerr, Daniel McGlynn

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


Prediction algorithms are increasingly popular techniques within the energy industry to assist in forecasting future prices and to help reduce costs. When predicting day-ahead energy prices several significant external factors that influence electricity prices need to be considered. Initially we use the transparent Nonlinear AutoRegressive Moving Average model with eXoge-nous inputs (NARMAX) to model the relationship of factors that contrib-ute to energy costs and day-ahead electricity prices. Energy data from 2017 were analyzed separately for Spring, Summer, and Autumn periods to ob-serve the effect of factors on seasonality. The seasonal NARMAX models identify important input factors and their correlated lagged values such as price, energy demand, generation type, and environmental temperature to be significant factors in accurately predicting day-ahead prices for all three seasons. The identified factors are used to refine a Seasonal AutoRegressive Integrated Moving Average model with eXogenous variables (SARIMAX). To conclude the seasonal NARMAX models proved useful for removing in-significant external factors and the seasonal refined SARIMAX models permitted accurate prediction of day-ahead prices.
Original languageEnglish
Title of host publicationInternational Conference on Time Series and Forecasting 2019
Subtitle of host publicationITISE 2019
Number of pages11
ISBN (Electronic)978-84-17970-78-9
Publication statusAccepted/In press - 19 Jul 2019
EventInternational Conference on Time Series and Forecasting - Granada, Spain
Duration: 25 Sept 201927 Sept 2019


ConferenceInternational Conference on Time Series and Forecasting
Abbreviated titleICTSF 2019


  • Energy Forecasting
  • Time-Series Modelling
  • Seasonality
  • Day-Ahead


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