基于时间卷积神经网络的非侵入式居民用电负荷分解方法

doi:10.12204/j.issn.1000-7229.2021.03.012

摘要/Abstract

摘要：

非侵入式负荷分解技术通过从主表信息中恢复出用电侧单个用电设备的状态,可以准确地刻画用户用电画像,为用户侧精细化管理发挥重要作用。针对目前人工神经网络模型在负荷分解中存在的分解精度不高、训练效率低下等问题,文章构建了基于时间卷积神经网络(temporal convolutional neural network,TCN)的非侵入式负荷分解模型。通过分析设备的用电规律,采用扩张因果卷积在主表功率序列进行卷积运算,扩大了感受野,提取到更加丰富的特征;通过增加残差连接,权重归一化层,优化训练数据窗口,提高了网络训练效率。最后,在经过优化的UKdale数据集上对构建的模型进行测试,实验结果表明获得的平均绝对误差、均方根误差、相对误差都处于较小的范围,时间复杂度分析也进一步说明了在不损失负荷分解精度的情况下,模型具有较短的训练时间。

关键词: 智能电网, 负荷分解, 时间卷积神经网络(TCN), 序列到点

Abstract:

Non-intrusive load disaggregation can accurately portray the user’s power consumption portrait by recovering the information of single electrical equipment on the power consumption side from the total electric meter information, which plays an important role in the refined management of the consumers. Aiming at the problems of low decomposition accuracy and low training efficiency of current artificial neural network models in load decomposition, this paper studies and builds a non-intrusive load disaggregation model based on temporal convolutional neural network. By analyzing the power consumption of the device, the dilated causal convolution is applied to perform convolution operations on the power sequence of electric meter and to expand the receptive field and extract richer features. The network training efficiency is improved by adding residual connections, weight normalization layers and optimizing training data window. Finally, the constructed model is tested on the optimized UKdale data set. The experimental results show that mean absolute error, root mean square error, and relative error are all in a relatively small range, and the time complexity analysis further shows that the model has a shorter training time without losing the load decomposition accuracy.

Key words: smart grid, load disaggregation, temporal convolutional neural network(TCN), sequence to point

中图分类号:

TM73

刘仲民, 侯坤福, 高敬更, 王治国. 基于时间卷积神经网络的非侵入式居民用电负荷分解方法[J]. 电力建设, 2021, 42(3): 97-106.

LIU Zhongmin, HOU Kunfu, GAO Jinggeng, WANG Zhiguo. Non-Intrusive Residential Electricity Load Disaggregation Based on Temporal Convolutional Neural Network[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 97-106.

图/表 20

图1 基于时间卷积神经网络的负荷分解流程

Fig.1 The process of NILM based on temporal convolutional neural network

图2 普通卷积与扩张因果卷积感受野对比

Fig.2 Comparison of the receptive field between ordinary convolution and dilated causal convolution

图3 冰箱运行特性

Fig.3 Refrigerator operating characteristic

图4 时间卷积神经网络结构

Fig.4 Structure of temporal convolutional neural network

表1 数据分配表

Table 1 Data configuration

表2 不同窗口长度实验结果对比

Table 2 Experimental results of different window lengths

图5 滑动窗口数据扩充示意图

Fig.5 Schematic diagram of sliding window for data augmentation

图6 家庭1冰箱负荷分解实验结果对比

Fig.6 Comparison of experimental results of refrigerator load monitoring in household 1

表3 1号家庭冰箱负荷分解评价指标对比

Table 3 Comparison of evaluation indices of refrigerator load monitoring in household 1

图7 跨家庭模型泛化能力实验结果对比

Fig.7 Comparison of experimental results of generalization ability of models across households

表4 跨家庭冰箱负荷分解评价指标对比

Table 4 Comparison of evaluation indices of refrigerator load monitoring across households

表5 训练时间(利用家庭1中3个月训练数据)

Table 5 Comparison of training time (3 months training data from household 1)

图A1 水壶实验结果对比显示

Fig.A1 Comparison of kettle experiment result

图A2 电视机实验结果对比

Fig.A2 Comparison of TV experiment results

表A1 水壶评价指标对比

Table A1 Comparison of kettle evaluation index

表A2 电视机评价指标对比

Table A2 Comparison of TV evaluation index

图A3 洗衣机实验结果对比显示

Fig.A3 Comparison of washing machine experiment results

图A4 洗碗机实验结果对比

Fig.A4 Comparison of dish washer experiment results

表A3 洗衣机评价指标对比

Table A3 Comparison of washing machine evaluation index

表A4 洗碗机评价指标对比

Table A4 Comparison of dish washer evaluation index

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