国能新疆甘泉堡综合能源有限公司;江苏科能电力工程咨询有限公司;中国能源建设集团江苏省电力设计院有限公司;河海大学电气与动力学院;
面向分钟级低频稳态负荷数据，提出了一种双分支特征集成长短记忆（long short-term memory,LSTM）神经网络的非侵入式负荷分解（non-intrusive load disaggregation,NILD）方法。按照分钟级低频采样数据对NILD的影响，将待分解的用电设备分为2类：第一类是持续运行时间较长，分钟级低频采样数据能够有效表征状态变化的用电设备；第二类是持续运行时间短、采用分钟级低频采样数据存在特征淹没的用电设备。针对第一类设备，将事件检测与分钟级负荷稳态功率分别作为输入特征，构建特征集成LSTM模型进行负荷；针对第二类设备，计及低频采样数据的特征淹没，采用差分滤波对负荷功率数据进行前置处理，然后与事件检测结果一起作为特征集成LSTM模型的输入。采用两种数据集对所提方法进行性能评估，实例证明特征集成LSTM模型在利用低频稳态数据进行NILD时具有一定的优越性。
| 153 | 0 | 142 |
| 下载次数 | 被引频次 | 阅读次数 |
[1]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.
[2]李子凯，岳宝强，杨波，等.适于低功率状态的多特征融合负荷分解方法[J].山东电力技术，2024,51(1):68-76.LI Zikai,YUE Baoqiang,YANG Bo,et al.Multi-feature fusion load disaggregation method for low-power states[J].Shandong Electric Power,2024,51(1):68-76.
[3]鲍海波，杨舒惠，陈子民，等.事件检测类非侵入式负荷监测算法综述[J].电力系统自动化，2023,47(13):94-109.BAO Haibo,YANG Shuhui,CHEN Zimin,et al.Review on eventinspection based non-intrusive load monitoring algorithms[J].Automation of Electric Power Systems,2023,47(13):94-109.
[4]弭辙，胡健祖，郭珍妮，等.新型电力系统体系下新能源发展态势及市场化消纳研究[J].山东电力技术，2023,50(10):1-8.MI Zhe,HU Jianzu,GUO Zhenni,et al. Research on the development situation and market-oriented consumption of renewable energy under new power system architecture[J].Shandong Electric Power,2023,50(10):1-8.
[5] HART G W. Prototype nonintrusive appliance load monitor[R].MIT Electric Power Research Institute Technical Report,1985:12-25.
[6] HART G W. Nonintrusive appliance load monitoring[J].Proceedings of the IEEE,1992,80(12):1870-1891.
[7] KIM H, MARWAH M, ARLITT M, et al. Unsupervised disaggregation of low frequency power measurements[C]?Proceedings of the 2011 SIAM International Conference on Data Mining. Philadelphia,PA:Society for Industrial and Applied Mathematics,2011:1-4.
[8] LUAN W P,YANG F,ZHAO B C,et al. Industrial load disaggregation based on Hidden Markov Models[J].Electric Power Systems Research,2022,210:108086.
[9] YAN L,TIAN W,HAN J Y,et al.eFHMM:event-based factorial hidden Markov model for real-time load disaggregation[J]. IEEE Transactions on Smart Grid,2022,13(5):3844-3847.
[10] CIANCETTA F,BUCCI G,FIORUCCI E,et al. A new convolutional neural network-based system for NILM applications[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1501112.
[11] LE T T H,HEO S,KIM H.Toward load identification based on the Hilbert transform and sequence to sequence long short-term memory[J].IEEE Transactions on Smart Grid,2021,12(4):3252-3264.
[12] YANG X,JIANG Q,SUN G P,et al.Simulation-data-driven load disaggregation based on multi-channel neural network for industrial and commercial users[J].IET Generation,Transmission&Distribution,2023,17(7):1652-1662.
[13] YANG D S,GAO X T,KONG L,et al. An event-driven convolutional neural architecture for non-intrusive load monitoring of residential appliance[J]. IEEE Transactions on Consumer Electronics,2020,66(2):173-182.
[14]杨秀，吴吉海，孙改平，等.基于深度学习和迁移学习的公共楼宇非侵入式负荷分解[J].电网技术，2022,46(3):1160-1168.YANG Xiu,WU Jihai,SUN Gaiping,et al. Non-intrusive load decomposition of public buildings based on deep learning and transfer learning[J]. Power System Technology,2022,46(3):1160-1168.
[15]王丹宇，刘君，周亚同，等.面向用电负荷分解的特征融合与Transformer模型[J].电力系统及其自动化学报，2023,36(6):129-136.WANG Danyu,LIU Jun,ZHOU Yatong,et al.Feature fusion and transformer model for electricity load decomposition[J].Proceedings of the CSU-EPSA,2023,36(6):129-136.
[16]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):135-144.GUO Hongxia,LU Jinwei,YANG Ping,et al. Review on key techniques of non-intrusive load monitoring[J]. Electric Power Automation Equipment,2021,41(1):135-144.
[17]周润，向月，王杨，等.基于智能电表集总数据的家庭电动汽车充电行为非侵入式辨识与负荷预测[J].电网技术，2022,46(5):1897-1906.ZHOU Run,XIANG Yue,WANG Yang,et al. Non-intrusive identification and load forecasting of household electric vehicle charging behavior based on smart meter data[J]. Power System Technology,2022,46(5):1897-1906.
[18] KONG W C,DONG Z Y,WANG B,et al.A practical solution for non-intrusive type II load monitoring based on deep learning and post-processing[J]. IEEE Transactions on Smart Grid,2020,11(1):148-160.
[19] ZHAO R,YAN R,WANG J,et al. Learning to monitor machine health with convolutional bi-directional LSTM networks[J].Sensors:Basel,Switzerland,2017,17(2):E273.
[20]马宗彪，许素安，朱少斌，等.基于特征加权模糊聚类的电力负荷分类[J].中国电力，2022,55(6):25-32.MA Zongbiao,XU Su’an,ZHU Shaobin,et al. Power load classification based on feature weighted fuzzy clustering[J].Electric Power,2022,55(6):25-32.
[21] KIM J,LE T T,KIM H. Nonintrusive load monitoring based on advanced deep learning and novel signature[J]. Computational Intelligence and Neuroscience,2017,2017:4216281.
[22] KOLTER J Z,JOHNSON M J.Redd:a public data set for energy disaggregation research[C]?Workshop on Data Mining Applications in Sustainability(SIGKDD). San Diego,CA,2011,25:59-62.
[23] BARKER S,MISHRA A,IRWIN D,et al.Smart:an open data set and tools for enabling research in sustainable homes[C]?SustKDD.August 2012:108.
[24] ZHUANG M M,SHAHIDEHPOUR M,LI Z Y.An overview of nonintrusive load monitoring:approaches,business applications,and challenges[C]?2018 International Conference on Power System Technology(POWERCON).IEEE,2018:4291-4299.
[25] RAIKER G A,REDDY S B,UMANAND L,et al.Approach to nonintrusive load monitoring using factorial hidden Markov model[C]?2018 IEEE 13th International Conference on Industrial and Information Systems(ICIIS).IEEE,2018:381-386.
基本信息:
DOI：10.20097/j.cnki.issn1007-9904.2024.10.004
中图分类号:TM714;TN713
引用信息:
[1]韩小地,郭家兴,钱康等.基于双分支特征集成LSTM的非侵入式负荷分解研究[J].山东电力技术,2024,51(10):31-43.DOI:10.20097/j.cnki.issn1007-9904.2024.10.004.
基金信息:
江苏省政府科技项目“可再生氢能制/储/管道掺混一体化场站成套设计与运行控制关键技术研发”（BE2022040）~~