| 曹雯,范冰,徐铭铭,景力涛,李德军,汤文俊.基于CNN-LSTM-CMA-GRU的多尺度中期负荷预测方法[J].电力需求侧管理,2026,28(2):57-63 |
| 基于CNN-LSTM-CMA-GRU的多尺度中期负荷预测方法 |
| Multi-scale medium-term load forecasting method based on CNN-LSTM-CMA-GRU |
| 投稿时间：2025-01-10 修订日期：2025-02-04 |
| DOI：10.3969/j.issn.1009-1831.2026.02.009 |
| 中文关键词: 中期负荷预测 交叉多头注意力 多时间尺度 CNN-LSTM 深度神经网络 |
| 英文关键词: medium-term load forecasting cross-multi-head attention multi-time scale CNN-LSTM deep neural network |
| 基金项目:国家电网有限公司总部科技项目(SGHADK00PJJS2200050) |
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| 中文摘要: |
| 精准的中期电力负荷预测对电力调度与资源优化至关重要。基于电力调度对日级负荷极值管理的实际需求,以日最大/最小负荷为预测粒度研究中期负荷预测。针对传统方法在长时间预测中因历史负荷和多维外部变量的耦合关系衰减导致的误差累积问题,提出一种融合交叉多头注意力机制(CMA)的深度神经网络时序预测方法。该模型采用三重创新设计:首先,双支卷积神经网络(CNN)-长短期记忆网络(LSTM)分别提取负荷序列的局部形态特征和外部变量的时序关联;其次,交叉多头注意力层建立历史负荷与未来时段外部变量的动态权重映射;最后,通过门控循环单元(GRU)实现多尺度特征的自适应融合。实验结果表明,该模型在中期电力负荷预测任务中表现出较高的准确性和鲁棒性。 |
| 英文摘要: |
| Accurate mid-term power load forecasting is crucial for power dispatch and resource optimization. Addressing the practical need for daily peak/valley load management in power scheduling, medium-term forecasting is studied with daily maximum/minimum load as the prediction granularity. To overcome the error accumulation caused by the decay of coupling relationships between historical loads and multi-dimensional external variables in traditional methods, a deep neural network time-series forecasting approach incorporating a cross multi-head attention (CMA) mechanism is proposed. The model features three innovative designs: first, a dual-branch convolutional neural network (CNN) and long short-term memory (LSTM) network are used to extract the local pattern features of the load sequence and the global temporal correlation of auxiliary variables; second, a cross-multi-head attention layer is designed to establish a dynamic weight mapping between historical load and external variables in future periods; finally, a gated recurrent unit (GRU) achieves adaptive fusion of multi-scale features. Experimental results demonstrate that the model achieves high accuracy and ro-bustness in power load forecasting tasks. |
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