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

arXiv: https://arxiv.org/abs/2311.07289 · HTML: https://mlsanity.com/paper/2311.07289/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets](https://mlsanity.com/paper/1905.07886/) (2019)

## Built on by (most cited first, 7 of 7)

- [Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression](https://mlsanity.com/paper/2404.02270/) (2024)
- [ReModels: Quantile Regression Averaging models](https://mlsanity.com/paper/2405.11372/) (2024)
- [Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression](https://mlsanity.com/paper/2412.00123/) (2024)
- [HADL Framework for Noise Resilient Long-Term Time Series Forecasting](https://mlsanity.com/paper/2502.10569/) (2025)
- [NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs](https://mlsanity.com/paper/2506.11050/) (2025)
- [The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets](https://mlsanity.com/paper/2511.05523/) (2025)
- [TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text](https://mlsanity.com/paper/2610.02256/) (2026)

## Abstract (arXiv)

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.