Hourly electricity price forecasting with NARMAX

Catherine McHugh, Sonya Coleman, Dermot Kerr

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Electricity price prediction through statistical and machine learning techniques captures market trends and would be a useful tool for energy traders to observe price fluctuations and increase their profits over time. A Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) identifies key energy-related factors that influence hourly electricity price through prediction modelling. We propose to use a transparent NARMAX model and analyse Irish Integrated Single Electricity Market (ISEM) data from May 2019 until April 2020 to determine which external factors have a significant impact on the electricity pricing. The experimental results indicate that historical electricity price, demand, and system generation are the most significant factors with historical electricity price being the most weighted factor and the largest Error Reduction Ratio (ERR). A NARMAX model generated using correlated lags was also considered to identify key energy-related lag factors that influence the electricity price. For justification, the significant lag factors are included as inputs in a Seasonal AutoRegressive Integrated Moving Average model with eXogenous input (SARIMAX) to determine if model performance improves with refinement. To conclude, using the NARMAX methodology with energy-related input factors helps to determine the significant factors and results in accurate predictions of electricity price.
Original languageEnglish
Article number100383
Number of pages8
JournalMachine Learning with Applications
Early online date11 Jul 2022
Publication statusPublished (in print/issue) - 15 Sept 2022

Bibliographical note

This research was funded by DfE CAST scholarship, UK in collaboration with Click Energy.


  • Energy market
  • Machine learning
  • Energy-related factors


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