1. 湖北省电力规划设计研究院有限公司
2. 广东工业大学自动化学院
3. 河海大学电气与动力工程学院
纸质出版:2025
移动端阅览
杨鑫源, 范传光, 叶佳锐, 等. 基于TCN-Attention模型的电力负荷短期概率预测方法[J]. 供用电, 2025,42(4):91-98.
杨鑫源, 范传光, 叶佳锐, 等. 基于TCN-Attention模型的电力负荷短期概率预测方法[J]. 供用电, 2025,42(4):91-98. DOI: 10.19421/j.cnki.1006-6357.2025.04.011.
精确预测电力负荷的短期趋势对于保持电力供需平衡至关重要。然而,电力用户的电力负荷数据通常包含短期和长期模式的混合,以及概率分布特性,这使得基于单一数据驱动的现有负荷预测方法难以应对其复杂性。提出了一种结合时间卷积网络(temporal convolutional network
TCN)和注意力机制(attention mechanism
AM)的新型神经网络模型,即TCN-Attention模型,旨在估计电力负荷的短期概率分布。该模型通过结合TCN和Attention机制,有效提取时间序列数据中表征突变和其他关键时间点的局部模式,以及表征趋势和周期性的全局模式。TCN主要负责提取时间序列的局部模式,而Attention机制则专注于提取全局模式,从而实现了对电力负荷特征的有效提取。最后,通过在加州大学尔湾分校(UCI)公开数据库中的电力负荷时间序列数据集上进行实验验证,结果表明所提出的TCN-Attention模型在电力负荷短期概率预测方面相较于现有方法具有更高的预测精度。
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