Performance of the autoregressive integrated moving average model with exogenous variables statistical model on the intraday market for the Denmark-West bidding area

This article aims to investigate whether a statistical model known as Autoregressive Integrated Moving Average with Explanatory Variables can aid better predictability of volume-weighted average electricity prices compared to a commonly used forecasting method. This analysis was conducted for a specific bidding area, the Denmark-West bidding area (DK1). Autoregressive integrated moving average model with exogenous variable's performance was tested on the DK1 intraday market over a two-year period starting from 1 January 2019 until 31 December 2020. An explanatory variable used to support better the accuracy of the forecast is the day-ahead price for a corresponding intraday delivery hour. To ensure the validity of the paper, a well-known forecasting methodology was applied, and the results of the analysis show superior performance over the benchmark forecasting method. The autoregressive integrated moving average model with exogenous variables model developed was found to significantly outperform other commonly used forecasting methods, with an average mean absolute percentage error of 1.5%. The model was able to accurately predict intraday volume-weighted average prices up to 24 h in advance, using only publicly available data on day-ahead prices and historical intraday prices. Energy traders and other market players may find the developed autoregressive integrated moving average model with exogenous variables model to be a useful resource when looking to make more informed decisions in the intraday market.

Automated summary

This article investigates the effectiveness of a statistical model called Autoregressive Integrated Moving Average with Explanatory Variables in predicting volume-weighted average electricity prices. The model is tested on the intraday market of the Denmark-West bidding area over a two-year period. Results show that the model outperforms other commonly used forecasting methods, with an average mean absolute percentage error of 1.5%. The developed model proves useful for energy traders and market players in making informed decisions in the intraday market.