融合外部注意力机制的序列到点非侵入式负荷分解

doi:10.16183/j.cnki.jsjtu.2022.534

摘要/Abstract

摘要：

非侵入式负荷分解可以深度挖掘用户电力消耗数据蕴含的信息价值,为电力设备故障监测、需求响应等决策分析提供重要参考.为有效解决非侵入式负荷分解算法训练时间成本与分解精度间的冲突,提出一种融合外部注意力机制的序列到点非侵入式负荷分解算法.首先,将总负荷功率消耗序列进行数据清理、标准化等预处理,以固定窗口长度构建训练输入数据,输入数据通过编码层自动提取设备特征;然后,设计外部注意力机制增强重要特征权值;最终,输入到解码层得到负荷分解结果.利用REDD与UK-DALE两种公开数据集进行模型仿真计算,在信号聚合误差、平均绝对误差、标准化分解误差指标、模型分解曲线、特征图和用户耗能等方面进行对比分析,本文模型克服了卷积层注意力分散的缺点,增强了对有效信息的提取与利用能力,在未增加训练时间成本的前提下具有更高的分解精度.

关键词: 非侵入式负荷分解, 外部注意力机制, 神经网络, 序列到点

Abstract:

Non-intrusive load disaggregation (NILD) can deeply explore the value of customer power consumption data, providing an important reference for decision analysis such as power equipment fault monitoring and demand response. Aimed at the conflict between the training time and the accuracy of non-intrusive load disaggregation, a non-intrusive load disaggregation algorithm using sequence-to-point integrating external attention (EA) mechanism is proposed. First, the original data is pre-processed by data purification, normalization, and some other operations, and the train data is built with a same length window. The equipment feature is extracted through the encoder layer. Then, the feature weights of important parts are enhanced by introducing an external attention mechanism. Finally, the results are yielded through the decoder layer. Simulation calculation of the proposed model and the current mainstream model is performed using the publicly available datasets, REDD and UK-DALE, while the indicators of signal aggregate error, mean absolute error, normalized disaggregation error, model disaggregation curves, feature map, and user energe consumption are compared and analyzed. The proposed model overcomes the shortcomings of attention scattering in the convolutional layer, enhances the ability to extract and utilize effective information, and has a more accurate decomposition accuracy without increasing the training time cost.

Key words: non-intrusive load disaggregation (NILD), external attention (EA) mechanism, neural networks, sequence-to-point (Seq2Point)

中图分类号:

TM721

李利娟, 刘海, 刘红良, 张青松, 陈永东. 融合外部注意力机制的序列到点非侵入式负荷分解[J]. 上海交通大学学报, 2024, 58(6): 846-854.

LI Lijuan, LIU Hai, LIU Hongliang, ZHANG Qingsong, CHEN Yongdong. Non-Intrusive Load Disaggregation Using Sequence-to-Point Integrating External Attention Mechanism[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 846-854.

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比例	8%	92%

设备	平均值/W	标准差/W
主电源	522	814
微波炉	500	800
冰箱	200	400
洗碗机	700	1000
洗衣机	400	700

设备	E_SAE				E_MAE				E_NDE
设备	CNN	T2V	CBAM	本文	CNN	T2V	CBAM	本文	CNN	T2V	CBAM	本文
微波炉	0.889	0.485	0.373	0.323	19.681	20.066	20.712	22.481	0.996	0.751	0.689	0.722
冰箱	0.219	0.209	0.159	0.092	35.69	31.076	37.603	28.494	0.338	0.279	0.345	0.231
洗碗机	0.478	0.643	0.395	0.358	19.142	29.610	19.476	18.365	0.505	0.872	0.437	0.431
洗衣机	0.053	0.095	0.089	0.045	15.106	12.427	19.065	18.622	0.199	0.139	0.172	0.174
均值	0.410	0.358	0.254	0.205	23.450	23.295	23.421	21.991	0.510	0.510	0.411	0.390

设备	E_SAE				E_MAE				E_NDE
设备	CNN	T2V	CBAM	本文	CNN	T2V	CBAM	本文	CNN	T2V	CBAM	本文
微波炉	0.100	0.877	0.719	0.028	5.732	10.318	7.447	6.042	0.613	0.984	0.606	0.594
冰箱	0.143	0.178	0.072	0.057	23.342	31.686	22.823	20.549	0.410	0.526	0.403	0.384
洗碗机	0.244	0.737	0.274	0.153	7.334	14.953	7.227	5.290	0.060	0.457	0.074	0.074
洗衣机	0.004	0.223	0.004	0.005	8.927	13.923	8.081	7.960	0.089	0.274	0.072	0.070
均值	0.122	0.504	0.267	0.061	11.333	17.720	11.394	9.960	0.293	0.560	0.288	0.280

模型	训练参数量	训练时间/s
CNN	30,708,249	123
CBAM	30,709,748	351
本文	29,229,199	120