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A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

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  • Cornell, Cameron
  • Dinh, Nam Trong
  • Pourmousavi, S. Ali

Abstract

The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training-length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin.

Suggested Citation

  • Cornell, Cameron & Dinh, Nam Trong & Pourmousavi, S. Ali, 2024. "A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1421-1437.
    • Handle: RePEc:eee:intfor:v:40:y:2024:i:4:p:1421-1437
    DOI: 10.1016/j.ijforecast.2023.12.003
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    Cited by:

    1. Meng, Anbo & Zhu, Jianbin & Yan, Baiping & Yin, Hao, 2024. "Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network," Applied Energy, Elsevier, vol. 369(C).

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