Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market

• URL: http://arxiv.org/abs/2604.23908v1
• Date: Sun, 26 Apr 2026 22:57:39 GMT
• Title: Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
• Abstract summary: Short term electricity price forecast is essential in competitive power markets.<n>But electricity price series exhibit high volatility, irregularity, and non-stationarity.<n>This study develops a unified benchmark framework to address these challenges.
• Score: 3.299203499767714
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non-stationarity. This phenomenon is pronounced in the South Australian region of the National Electricity Market, where high renewable penetration drives price volatility and frequent negative price intervals, while structural changes such as the transition to five-minute settlement further complicate forecast. To address these challenges, this study develops a unified benchmark framework. Under identical data preprocessing, feature engineering with lag features, rolling statistics, cyclic temporal encodings, and so on, and an 85% to 15% chronological train test split, six algorithms are systematically compared, including AWMLSTM, CatBoost, GBRT, LSTM, LightGBM, and SVR. The results show that for price prediction, tree-based models, especially GBRT with an R squared value of 0.88, generally outperform LSTM and SVR. However, all models achieve a mean absolute percentage error above 90%, and more than 65% of GBRT predictions have relative errors above 10%, which highlights the inherent difficulty of price forecast. For demand prediction, all models perform substantially better than in price prediction. AWMLSTM and GBRT achieve an R2 value of 0.96 with mean absolute percentage error below 32%, and GBRT has 74.37% of samples within 5% error, while LSTM and SVR perform less accurately in both tasks. Future improvements should focus on hybrid models such as tree plus transformers, data augmentation for extreme events, and error correction to better capture price spikes.

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