上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (6): 846-854.doi: 10.16183/j.cnki.jsjtu.2022.534
收稿日期:
2022-12-29
修回日期:
2023-04-26
接受日期:
2023-05-22
出版日期:
2024-06-28
发布日期:
2024-07-05
通讯作者:
刘红良,教授,博士生导师;E-mail: 作者简介:
李利娟(1980-),教授,博士生导师,从事电力系统脆弱性研究.
基金资助:
LI Lijuan1, LIU Hai1, LIU Hongliang2(), ZHANG Qingsong1, CHEN Yongdong1
Received:
2022-12-29
Revised:
2023-04-26
Accepted:
2023-05-22
Online:
2024-06-28
Published:
2024-07-05
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.
中图分类号:
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.
表2
REDD数据集各模型对比
设备 | ESAE | EMAE | ENDE | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
表3
UK-DALE数据集合各模型对比
设备 | ESAE | EMAE | ENDE | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
[1] | 刘博, 栾文鹏. 基于负荷分解的用电数据云架构方案及应用场景[J]. 电网技术, 2016, 40(3): 791-796. |
LIU Bo, LUAN Wenpeng. Conceptual cloud solution architecture and application scenarios of power consumption data based on load disaggregation[J]. Power System Technology, 2016, 40(3): 791-796. | |
[2] | 郭红霞, 陆进威, 杨苹, 等. 非侵入式负荷监测关键技术问题研究综述[J]. 电力自动化设备, 2021, 41(1): 135-146. |
GUO Hongxia, LU Jinwei, YANG Ping, et al. Review on key techniques of non-intrusive load monito-ring[J]. Electric Power Automation Equipment, 2021, 41(1): 135-146. | |
[3] | HART G W. Nonintrusive appliance load monitoring[J]. Proceedings of the IEEE, 1992, 80(12): 1870-1891. |
[4] | 邓晓平, 张桂青, 魏庆来, 等. 非侵入式负荷监测综述[J]. 自动化学报, 2022, 48(3): 644-663. |
DENG Xiaoping, ZHANG Guiqing, WEI Qinglai, et al. A survey on the non-intrusive load monitoring[J]. Acta Automatica Sinica, 2022, 48(3): 644-663. | |
[5] | 程祥, 李林芝, 吴浩, 等. 非侵入式负荷监测与分解研究综述[J]. 电网技术, 2016, 40(10): 3108-3117. |
CHENG Xiang, LI Linzhi, WU Hao, et al. A survey of the research on non-intrusive load monitoring and disaggregation[J]. Power System Technology, 2016, 40(10): 3108-3117. | |
[6] | WICHAKOOL W, REMSCRIM Z, ORJI U A, et al. Smart metering of variable power loads[J]. IEEE Transactions on Smart Grid, 2015, 6(1): 189-198. |
[7] | PATEL S N, ROBERTSON T, KIENTZ J A, et al. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line (nominated for the best paper award)[C]//International Conference on Ubiquitous Computing. Berlin, Germany: Springer, 2007: 271-288. |
[8] | 刘然. 结合改进最近邻法与支持向量机的住宅用电负荷识别研究[D]. 重庆: 重庆大学, 2014. |
LIU Ran. Research on household load identification combining improved nearest neighbour method and support vector machine[D]. Chongqing: Chongqing University, 2014. | |
[9] | FIGUEIREDO M, RIBEIRO B, DE ALMEIDA A. Electrical signal source separation via nonnegative tensor factorization using on site measurements in a smart home[J]. IEEE Transactions on Instrumentation & Measurement, 2014, 63(2): 364-373. |
[10] | 祁兵, 程媛, 武昕. 基于Fisher有监督判别的非侵入式居民负荷辨识方法[J]. 电网技术, 2016, 40(8): 2484-2491. |
QI Bing, CHENG Yuan, WU Xin. Non-intrusive household appliance load identification method based on fisher supervised discriminant[J]. Power System Technology, 2016, 40(8): 2484-2491. | |
[11] | CHOU P A, CHANG R I. Unsupervised adaptive non-intrusive load monitoring system[C]//2013 IEEE International Conference on Systems, Man, and Cybernetics. Manchester, UK: IEEE, 2013: 3180-3185. |
[12] | 李如意, 王晓换, 胡美璇, 等. RPROP神经网络在非侵入式负荷分解中的应用[J]. 电力系统保护与控制, 2016, 44(7): 55-61. |
LI Ruyi, WANG Xiaohuan, HU Meixuan, et al. Application of RPROP neural network in nonintrusive load decomposition[J]. Power System Protection & Control, 2016, 44(7): 55-61. | |
[13] | BATRA N, KELLY J, PARSON O, et al. NILMTK: An open source toolkit for non-intrusive load monitoring[C]//Proceedings of the 5th international conference on Future energy systems. Cambridge, UK: ACM, 2014: 265-276. |
[14] | FIGUEIREDO M, DE ALMEIDA A, RIBEIRO B. Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems[J]. Neurocomputing, 2012, 96: 66-73. |
[15] | 涂京, 周明, 宋旭帆, 等. 基于监督学习的非侵入式负荷监测算法比较[J]. 电力自动化设备, 2018, 38(12): 128-134. |
TU Jing, ZHOU Ming, SONG Xufan, et al. Comparison of supervised learning-based non-intrusive load monitoring algorithms[J]. Electric Power Automation Equipment, 2018, 38(12): 128-134. | |
[16] | KARMAKAR P, TENG S W, LU G J. Thank you for attention: A survey on attention-based artificial neural networks for automatic speech recognition[DB/OL]. (2021-02-14)[2022-05-01]. https://arxiv.org/abs/2102.07259. |
[17] | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: ACM, 2017: 6000-6010. |
[18] | MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[DB/OL]. (2013-01-16)[2022-05-01]. https://arxiv.org/abs/1301.3781. |
[19] | KELLY J, KNOTTENBELT W. Neural NILM: Deep neural networks applied to energy disaggregation[C]//Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments. Seoul, South Korea: ACM, 2015: 55-64. |
[20] | ZHANG C Y, ZHONG M J, WANG Z Z, et al. Sequence-to-point learning with neural networks for non-intrusive load monitoring[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, California, USA: AAAI, 2018: 2604-2611. |
[21] | PAN Y G, LIU K, SHEN Z Y, et al. Sequence-to-subsequence learning with conditional Gan for power disaggregation[C]//ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona, Spain:IEEE, 2020: 3202-3206. |
[22] | YANG M Z, LIU Y E, LIU Q L. Nonintrusive residential electricity load decomposition based on transfer learning[J]. Sustainability, 2021, 13(12): 6546. |
[23] | KAZEMI S M, GOEL R, EGHBALI S, et al. Time2Vec: Learning a vector representation of time[DB/OL]. (2019-07-11)[2022-05-01]. https://arxiv.org/abs/1907.05321. |
[24] | 徐晓会, 赵书涛, 崔克彬. 基于卷积块注意力模型的非侵入式负荷分解算法[J]. 电网技术, 2021, 45(9): 3700-3706. |
XU Xiaohui, ZHAO Shutao, CUI Kebin. Non-intrusive load disaggregate algorithm based on convolutional block attention module[J]. Power System Technology, 2021, 45(9): 3700-3706. | |
[25] | KOLTER J Z, JOHNSON M J. REDD: A public data set for energy disaggregation research[EB/OL]. (2011-01-01)[2022-05-01]. https://people.csail.mit.edu/mattjj/papers/kddsust2011.pdf. |
[26] | KELLY J, KNOTTENBELT W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes[J]. Scientific Data, 2015, 2: 150007. |
[27] | GUO M H, LIU Z N, MU T J, et al. Beyond self-attention: External attention using two linear layers for visual tasks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2023, 45(5): 5436-5447. |
[28] | GUO M H, CAI J X, LIU Z N, et al. PCT: Point cloud transformer[J]. Computational Visual Media, 2021, 7(2): 187-199. |
[29] | PRECHELT L. Early stopping-but when?[M]//Lecture Notes in Computer Science. Berlin, Germany: Springer Berlin Heidelberg, 1998: 55-69. |
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