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

机器学习模型在预测波动剧烈的澳大利亚电力价格方面面临困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

一项新研究对澳大利亚国家电力市场六种用于短期电力价格预测的机器学习模型进行了基准测试。研究强调，由于价格波动剧烈、模式不规则以及市场结构性变化，预测面临重大挑战。与LSTM和SVR相比，GBRT等基于树的模型在价格预测方面表现更优，R平方值达到0.88，但总体预测准确性仍然较低，误差率很高。 AI

影响强调了将当前机器学习模型应用于波动剧烈的能源市场所面临的困难，并为未来的改进提出了混合模型和数据增强的建议。

排序理由在arXiv上发表的学术论文，详细介绍了机器学习模型在特定预测任务中的性能。

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arXiv cs.LG TIER_1 English(EN) · Wei Lu, Jay Wang, Dingli Duan, Ding Mao, Caiyi Song, John Huang · 2026-04-28 04:00

澳大利亚国家电力市场短期电力价格预测的机器学习与深度学习模型

arXiv:2604.23908v1 Announce Type: new 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 …