基于MTS-BiGRU-DMHSA的工业负荷预测方法

doi:10.19678/j.issn.1000-3428.0068641

摘要/Abstract

摘要：

工业用电占我国全社会用电量比重大, 通过工业负荷预测了解负荷趋势和用电量信息, 有助于电网安全稳定运行, 为电力部门发电规划提供依据, 且有助于工业用户优化生产工艺和降低成本。为了兼顾工业负荷波动的不确定性以及工业用户用电行为的规律性特征, 提出一种基于多时间尺度(MTS)特征的工业负荷预测方法MTS-BiGRU-DMHSA, 利用MTS特征融合挖掘工业负荷的周期趋势特征和局部波动特征, 提升工业负荷表征的可解释性。此外, 双层多头自注意力(DMHSA)机制利用注意力权重聚焦重要特征, 在挖掘输入特征关联性的同时捕捉时序关联性, 强化重要特征变量与关键时间步的信息表达。在中国某工业企业五面受总柜实采数据上完成实验验证, 采用2种评价指标对所提方法及5种基于神经网络的预测方法进行对比分析。实验结果表明, 所提方法相较于对比方法平均误差降低逾20%, 其中4.67%得益于对MTS特征的运用。通过对比各方法计算效率证实了所提方法的综合性能优势, 可视化实验结果与对比分析也验证了其在工业负荷预测任务上的可行性。

关键词: 工业负荷预测, 神经网络, 多时间尺度特征, 注意力机制, 时间序列分析

Abstract:

Industrial electricity consumption constitutes a significant portion of total electricity usage in China. Understanding load trends and electricity consumption data through industrial load forecasting is essential for ensuring the safe and stable operation of power grids, guiding power generation planning, and aiding industrial users in optimizing production processes and reducing costs. To address the uncertainty in industrial load fluctuations while considering electricity consumption behavior of regular industrial users, a Multi-Time Scale (MTS) feature-based industrial load forecasting method, MTS Bidirectional Gated Recurrent Unit Dual-layer Multi-Head Self-Attention (MTS-BiGRU-DMHSA), is proposed. The MTS feature fusion approach mines periodic trends and local fluctuations in industrial loads, enhancing the interpretability of load characterization. Additionally, the DMHSA mechanism utilizes attention weights to highlight important features, capturing temporal correlations through input feature correlation mining, and improving the expression of significant feature variables and key time steps. This study carries out experimental validation using data from the five-sided receiving cabinet of an industrial enterprise in China. The proposed method is compared against two evaluation indicators and five neural network-based prediction methods. The comprehensive performance advantages of the proposed method are confirmed through comparisons of computational efficiency. Results demonstrate a reduction in the error index by an average of more than 20%, with a 4.67% improvement attributed to the use of MTS features. The feasibility of applying this method to industrial load forecasting tasks is validated through visualized results and comparative analysis.

Key words: industrial load forecasting, neural network, Multi-Time Scale(MTS) feature, attention mechanism, time series analysis

王汝英, 马嘉骏, 董建强, 刘万龙, 张海涛, 尹凯, 赵博超. 基于MTS-BiGRU-DMHSA的工业负荷预测方法[J]. 计算机工程, 2024, 50(9): 169-178.

WANG Ruying, MA Jiajun, DONG Jianqiang, LIU Wanlong, ZHANG Haitao, YIN Kai, ZHAO Bochao. Industrial Load Forecasting Method Based on MTS-BiGRU-DMHSA[J]. Computer Engineering, 2024, 50(9): 169-178.

https://www.ecice06.com/CN/Y2024/V50/I9/169

图/表 10

图1 工业设备有功功率信号

Fig.1 Active power signal of industrial equipments

图2 多时间尺度功率信号输入示意图

Fig.2 Schematic diagram of multi-time scale power signal inputs

图3 门控循环单元网络结构

Fig.3 Structure of gate recurrent unit network

图4 BiGRU网络结构

Fig.4 Structure of BiGRU network

图5 多头自注意力机制结构

Fig.5 Structure of multi-head self-attention mechanism

图6 MTS-BiGRU-DMHSA模型结构

Fig.6 Structure of MTS-BiGRU-DMHSA model

图7 不同方法的预测结果对比

Fig.7 Comparison of prediction results for different methods

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