Daily Energy Price Forecasting Using a Polynomial NARMAX Model

Catherine McHugh, Sonya Coleman, Dermot Kerr, Daniel McGlynn

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Energy prices are not easy to forecast due to nonlinearity from seasonal trends. In this paper a Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX model) is created using nonlinear energy price data. To investigate if a short-term forecasting model is capable of pre-dicting energy prices a model was developed using daily data from 2017 over a period of five weeks: observing 1 input lag prediction up to 12 input lag prediction for low-order polynomials (linear, quadratic, and cubic). Var-ious input factors were explored (energy demand and previous price) with different combinations to observe which factors, if any, had an impact on the current price prediction. The results show that the generated NARMAX model is good at describing the input-output relationship of energy prices. The model works best with a low-order input regression parameter and line-ar polynomial degree. It was also noted that including energy demand as an input factor slightly improves the model validation results suggesting that there is a relationship between demand and energy prices.
Original languageEnglish
Number of pages12
Publication statusAccepted/In press - 24 May 2018
Event18th Annual UK Workshop on Computational Intelligence - Nottingham University, Nottingham, United Kingdom
Duration: 5 Sept 20187 Sept 2018


Conference18th Annual UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2018
Country/TerritoryUnited Kingdom
Internet address


  • NARMAX modelling
  • Machine Learning
  • Energy price forecasting
  • Polynomial


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