对比六种模型，发现树模型在澳洲电力价格预测中表现更优，但整体难度仍高。
Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market
- 统一框架下比较六类模型，用滞后特征和时间编码提升输入质量。
- 树模型如GBRT的R²达0.88，但价格预测平均误差超90%，超65%预测偏差>10%。
- 需求预测远优于价格预测，适合关注电力市场建模的研究者参考。
短期电价预测对竞争性电力市场至关重要，但电价序列具有高度波动性、不规则性和非平稳性。这一现象在澳大利亚全国电力市场南澳区域尤为显著，高可再生能源渗透率导致价格波动剧烈并频繁出现负电价，而五分钟结算机制等结构性变化进一步加剧了预测难度。为应对这些挑战，本研究构建了一个统一基准框架，在相同的预处理、特征工程（包括滞后特征、滚动统计量、周期性时间编码等）及85%至15%的时间顺序划分训练测试集条件下，系统比较了六种算法：AWMLSTM、CatBoost、GBRT、LSTM、LightGBM和SVR。结果表明，在电价预测方面，树模型整体优于LSTM和SVR，其中GBRT的R²达到0.88；然而所有模型的平均绝对百分比误差均超过90%，且超过65%的GBRT预测相对误差大于10%，凸显预测本质困难。在需求预测方面，所有模型表现显著优于电价预测：AWMLSTM与GBRT的R²达0.96，平均绝对百分比误差低于32%，且GBRT有74.37%的样本误差在5%以内；相比之下，LSTM与SVR在两项任务中表现较弱。未来改进应聚焦于混合模型（如树+变压器）、极端事件的数据增强以及误差校正，以更好捕捉电价尖峰。
原文摘要 · Abstract (English)
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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