Daily Energy Price Forecasting Using a Polynomial NARMAX Model

Research output: Contribution to conferencePaper

Abstract

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.

Conference

Conference18th Annual UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2018
CountryUnited Kingdom
CityNottingham
Period5/09/187/09/18
Internet address

Fingerprint

Polynomials
Statistical Models

Keywords

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

Cite this

McHugh, C., Coleman, S., Kerr, D., & McGlynn, D. (Accepted/In press). Daily Energy Price Forecasting Using a Polynomial NARMAX Model. Paper presented at 18th Annual UK Workshop on Computational Intelligence, Nottingham, United Kingdom.
McHugh, Catherine ; Coleman, Sonya ; Kerr, Dermot ; McGlynn, Daniel. / Daily Energy Price Forecasting Using a Polynomial NARMAX Model. Paper presented at 18th Annual UK Workshop on Computational Intelligence, Nottingham, United Kingdom.12 p.
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title = "Daily Energy Price Forecasting Using a Polynomial NARMAX Model",
abstract = "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.",
keywords = "NARMAX modelling , Machine Learning, Energy price forecasting, Polynomial",
author = "Catherine McHugh and Sonya Coleman and Dermot Kerr and Daniel McGlynn",
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url = "http://ukci2018.uk",

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McHugh, C, Coleman, S, Kerr, D & McGlynn, D 2018, 'Daily Energy Price Forecasting Using a Polynomial NARMAX Model' Paper presented at 18th Annual UK Workshop on Computational Intelligence, Nottingham, United Kingdom, 5/09/18 - 7/09/18, .

Daily Energy Price Forecasting Using a Polynomial NARMAX Model. / McHugh, Catherine; Coleman, Sonya; Kerr, Dermot; McGlynn, Daniel.

2018. Paper presented at 18th Annual UK Workshop on Computational Intelligence, Nottingham, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Daily Energy Price Forecasting Using a Polynomial NARMAX Model

AU - McHugh, Catherine

AU - Coleman, Sonya

AU - Kerr, Dermot

AU - McGlynn, Daniel

PY - 2018/5/24

Y1 - 2018/5/24

N2 - 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.

AB - 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.

KW - NARMAX modelling

KW - Machine Learning

KW - Energy price forecasting

KW - Polynomial

M3 - Paper

ER -

McHugh C, Coleman S, Kerr D, McGlynn D. Daily Energy Price Forecasting Using a Polynomial NARMAX Model. 2018. Paper presented at 18th Annual UK Workshop on Computational Intelligence, Nottingham, United Kingdom.