| 江 山,李 卫,汤子琪.基于多尺度权重适配和双向门控循环单元的工业企业短期电力负荷预测方法[J].电力需求侧管理,2025,27(6):99-105 |
| 基于多尺度权重适配和双向门控循环单元的工业企业短期电力负荷预测方法 |
| Short-term power load forecasting method for industry based on multi scale weight adaptation and bidirectional gated recurrent unit |
| 投稿时间：2025-05-21 修订日期：2025-08-16 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 06. 015 |
| 中文关键词: 短期负荷预测 多尺度特征 权重适配 双向门控循环单元 特征嵌入 工业企业 |
| 英文关键词: short term load forecasting multi scale features weight adaptation bidirectional gated recurrent unit feature embedding industry enterprises |
| 基金项目:上海市促进产业高质量发展专项（人工智能专题）（2021-GZL-RGZN-01005） |
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| 中文摘要: |
| 工业企业历史用电负荷数据具有波动性强与时序性复杂的特点，为准确预测用电负荷带来了一定的挑战。针对此问题，提出一种基于多尺度权重适配和双向门控循环单元（multi scale weight adaptation and bidirectional gated recurrent unit，MSWA-Bi?GRU）的工业企业短期负荷预测方法。模型由权重适配层、BiGRU层、特征嵌入层与全连接预测层组成。首先，利用权重适配层自适应生成对不同时间尺度负荷数据的依赖热力系数进行加权，然后基于BiGRU层同时学习历史负荷序列多尺度上的瞬态波动性特征与稳态周期性特征，再利用特征嵌入层对其他特征进行建模，最终将时序特征与其他特征融合，由全连接预测层获得最终负荷预测结果。通过真实用电负荷数据进行案例分析，结果表明，所提出方法的预测性能优于其他方法，验证了该方法的有效性与可行性。 |
| 英文摘要: |
| Historical electricity load data of industrial enterprises has the characteristics of strong volatility and complex sequence, which brings challenges for accurately predicting electricity load. In order to solve these problems, a short-term load forecasting method for industry based on multi scale weight adaptation and bidirectional gated recurrent unit（MSWA BiGRU）is proposed. The proposed model is composed by a weight adaptation layer, a BiGRU layer, a feature embedding layer, and a fully connected prediction layer. Firstly, the weight adaptation layer adaptively generates the dependent thermal coefficients for different time scale load data, and then the BiGRU layer simultaneously learnes the transient fluctuation characteristics and steady-state periodic characteristics of the historical load series on multiple scales. Then, other features is embedded in the feature embedding layer. Finally, the temporal features are fused with other features to obtain the final load prediction result through the fully connected prediction layer. Experimental results on real data of electricity load in industrial and commercial enterprises show that the prediction performance of the proposed method is superior to other methods,thus the effectiveness and feasibility of this method are verified. |
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