【用电负荷特性分析技术研究现状的文献综述2800字】

导论用电负荷特性分析技术研究现状的文献综述用电负荷特性分析技术，是一种通过分析用电负荷的特性对设备能耗、设备状态等信息进行监测的技术。根据采集用电负荷数据信息的位置不同，可将其划分为两种形式：侵入式与非侵入式。侵入式的用电负荷特性分析技术是为台区内的每个用电设备装配一个电力参数监测装置收集用电负荷信息。非侵入式的用电负荷特性分析技术，也被称非侵入式负荷监测（Non-IntrusiveLoadMonitoring，NILM）技术ADDINZOTERO_ITEMCSL_CITATION{"citationID":"iZD2xsk4","properties":{"formattedCitation":"\\super[28]\\nosupersub{}","plainCitation":"[28]","noteIndex":0},"citationItems":[{"id":921,"uris":["/users/7647885/items/C5HJ8V3U"],"uri":["/users/7647885/items/C5HJ8V3U"],"itemData":{"id":921,"type":"article-journal","container-title":"ProceedingsoftheIEEE","DOI":"10/cpp2rx","ISSN":"00189219","issue":"12","journalAbbreviation":"Proc.IEEE","language":"en","page":"1870-1891","source":"DOI.org(Crossref)","title":"Nonintrusiveapplianceloadmonitoring","volume":"80","author":[{"family":"Hart","given":"G.W."}],"issued":{"date-parts":[["1992",12]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[28]，该技术利用智能分析装置在区域总进电处获取电流、电压等电力数据信息，通过处理与分析，监测某一特定区域内各种用电设备的负荷信息，从而获取各类设备所处的工作状态、分项耗电信息和用电规律等，实现用电负荷分项采集和统计的功能ADDINZOTERO_ITEMCSL_CITATION{"citationID":"580V3KDh","properties":{"formattedCitation":"\\super[29,30]\\nosupersub{}","plainCitation":"[29,30]","noteIndex":0},"citationItems":[{"id":541,"uris":["/users/7647885/items/UDDJSCKN"],"uri":["/users/7647885/items/UDDJSCKN"],"itemData":{"id":541,"type":"article-journal","abstract":"非侵入式居民电力负荷监测与分解技术是一种全新的监测负荷耗电细节的技术。居民用电细节监测在电力公司优化电网的规划、运行与管理,电力用户节省耗电量和电费,以及全社会把提高生态文明意识付诸实践等方面具有重要意义。在这种用电细节监测中,非侵入式方法同侵入式方法相比具有简单、经济、可靠和易于迅速推广应用等优势。从实现非侵入式居民电力负荷监测与分解的基本依据——负荷印记入手,简述了基本原理、负荷分解模型和求解方法。该项技术有望发展成为新一代智能电表的核心技术。鉴于居民电力负荷组成最为复杂,该技术亦可用于工业和商业负荷用电细节的监测。","container-title":"南方电网技术","DOI":"10/gjtdtc","ISSN":"1674-0629","issue":"04","language":"中文;","note":"122citations(CNKI)[2021-04-28]","page":"1-5","source":"CNKI","title":"非侵入式居民电力负荷监测与分解技术","volume":"7","author":[{"family":"余","given":"贻鑫"},{"family":"刘","given":"博"},{"family":"栾","given":"文鹏"}],"issued":{"date-parts":[["2013"]]}},"label":"page"},{"id":551,"uris":["/users/7647885/items/8ZKL99GM"],"uri":["/users/7647885/items/8ZKL99GM"],"itemData":{"id":551,"type":"article-journal","abstract":"非侵入式负荷监测通过对总负荷电表数据进行分析处理,能够实现对各个用电设备及其工作状态的辨识,可广泛应用于建筑节能、智慧城市、智能电网等领域.近年来,随着智能电表的大规模部署以及各类机器学习算法的广泛应用,非侵入式负荷监测引起了学术界与工业界的共同关注.本文对非侵入式负荷监测方面的研究进行综述.首先提炼非侵入式负荷监测的问题模型及基本框架;然后分别对非侵入式负荷监测的数据采集与预处理过程、负荷分解模型与方法、常用数据集及评估指标进行归纳总结;最后,对目前研究中存在的挑战进行分析,并对未来的研究方向进行展望.","container-title":"自动化学报","DOI":"10/gjtf5r","ISSN":"0254-4156","language":"中文","note":"<北大核心,EI,CSCD>","page":"1-21","source":"CNKI","title":"非侵入式负荷监测综述","author":[{"family":"邓","given":"晓平"},{"family":"张","given":"桂青"},{"family":"魏","given":"庆来"},{"family":"彭","given":"伟"},{"family":"李","given":"成栋"}]},"label":"page"}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[29,30]。非侵入式的用电负荷特性分析技术相较于侵入式，具备简单、经济、易于推广等优势，逐渐成为主流的用电负荷特性分析技术ADDINZOTERO_ITEMCSL_CITATION{"citationID":"y80PI7yl","properties":{"formattedCitation":"\\super[29]\\nosupersub{}","plainCitation":"[29]","noteIndex":0},"citationItems":[{"id":541,"uris":["/users/7647885/items/UDDJSCKN"],"uri":["/users/7647885/items/UDDJSCKN"],"itemData":{"id":541,"type":"article-journal","abstract":"非侵入式居民电力负荷监测与分解技术是一种全新的监测负荷耗电细节的技术。居民用电细节监测在电力公司优化电网的规划、运行与管理,电力用户节省耗电量和电费,以及全社会把提高生态文明意识付诸实践等方面具有重要意义。在这种用电细节监测中,非侵入式方法同侵入式方法相比具有简单、经济、可靠和易于迅速推广应用等优势。从实现非侵入式居民电力负荷监测与分解的基本依据——负荷印记入手,简述了基本原理、负荷分解模型和求解方法。该项技术有望发展成为新一代智能电表的核心技术。鉴于居民电力负荷组成最为复杂,该技术亦可用于工业和商业负荷用电细节的监测。","container-title":"南方电网技术","DOI":"10/gjtdtc","ISSN":"1674-0629","issue":"04","language":"中文;","note":"122citations(CNKI)[2021-04-28]","page":"1-5","source":"CNKI","title":"非侵入式居民电力负荷监测与分解技术","volume":"7","author":[{"family":"余","given":"贻鑫"},{"family":"刘","given":"博"},{"family":"栾","given":"文鹏"}],"issued":{"date-parts":[["2013"]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[29]。根据负荷监测任务的目标不同，用电负荷特性分析技术可被划分为基于事件的和基于状态的。基于事件的用电负荷特性分析技术以用电设备的投切为关注点，将进线处的电力参数变化情况与设备的开关过程相对应，从而推测出用电设备的能耗数据信息；基于状态的用电负荷特性分析技术以用电设备的状态为目标变量，求解关于设备功耗估值最接近实际值的优化问题ADDINZOTERO_ITEMCSL_CITATION{"citationID":"CelHY09S","properties":{"formattedCitation":"\\super[31]\\nosupersub{}","plainCitation":"[31]","noteIndex":0},"citationItems":[{"id":170,"uris":["/users/7647885/items/YDI939Z2"],"uri":["/users/7647885/items/YDI939Z2"],"itemData":{"id":170,"type":"thesis","abstract":"随着国家电网提出了建设泛在电力物联网的战略目标,用户内部设备的信息的感知和获取是提供智慧新业务的基础,而非侵入式负荷监测技术则是可以实现这一目的合适的智能感知方案。非侵入式负荷监测指的是将在用户总进线处获得的功耗聚合数据分解为各个电器设备运行功耗的技术。本文研究了两种不同类型的非侵入式负荷监测算法;基于事件的和基于状态的。针对于基于事件的非侵入式负荷监测算法的四个环节（事件检测、特征提取、负荷识别和能耗分解）提出了基于机器学习的不同的算法,并且都通过场景实验和公开数据集BLUED和ECO验证了算法的有效性。另外,本文还以隐马尔可夫模型为例研究了基于状态的非侵入式负荷监测,以探索两种不同类型方法的优劣势以及适用情况。整篇文章的工作具体由以下三部分组成。首先,本文针对基于事件的非侵入式负荷监测的事件检测环节提出了一种混合事件检测方法。它利用了多个不同方法的事件检测器以解决噪声对于事件检测的干扰。经过算例和实验分析,它比单一的事件检测方法在复杂的场景具有更好的检测效果。其次,在基于事件方法的负荷识别和能耗分解的环节中,本文分别提出了一种基于DBSCAN的无监督负荷识别方法和一种基于运行时间窗口的能耗分解方法。基于DBSCAN的无监督负荷识别方法在聚类的过程引入了对噪声的考虑,并将设备划分为周期型设备与用户驱动型设备。而基于运行时间窗口的能耗分解方法用以解决修复在能耗分解中被遗漏检测或是错误识别的事件产生不良影响。这两个算法的有效性都通过在公开数据集下得到了验证。最后,本文以隐马尔可夫模型为例,研究了基于状态的非侵入式负荷检测。通过实验,比较了它与基于事件的方法在不同采集频率下的监测效果,分析出它们分别适用的场景。","genre":"硕士","language":"中文;","note":"DOI:10.27461/ki.gzjdx.2020.000897","publisher":"浙江大学","source":"CNKI","title":"基于机器学习的非侵入式负荷监测技术研究","URL":"/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD202002&filename=1020768289.nh&v=efIoR01DQIrDWNIQBYTsfUmIgB%25mmd2FhAqAW0CAGyW9x5xv39%25mmd2FaqlDNcaLQZcPZrNe8M","author":[{"family":"徐","given":"志翔"}],"accessed":{"date-parts":[["2021",3,14]]},"issued":{"date-parts":[["2020"]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[31]。基于事件的用电负荷特性分析技术研究现状基于事件的用电负荷特性分析技术的典型流程主要包括数据采集与处理、事件检测、特征提取、负荷识别等步骤ADDINZOTERO_ITEMCSL_CITATION{"citationID":"UYJMbB0j","properties":{"formattedCitation":"\\super[30]\\nosupersub{}","plainCitation":"[30]","noteIndex":0},"citationItems":[{"id":551,"uris":["/users/7647885/items/8ZKL99GM"],"uri":["/users/7647885/items/8ZKL99GM"],"itemData":{"id":551,"type":"article-journal","abstract":"非侵入式负荷监测通过对总负荷电表数据进行分析处理,能够实现对各个用电设备及其工作状态的辨识,可广泛应用于建筑节能、智慧城市、智能电网等领域.近年来,随着智能电表的大规模部署以及各类机器学习算法的广泛应用,非侵入式负荷监测引起了学术界与工业界的共同关注.本文对非侵入式负荷监测方面的研究进行综述.首先提炼非侵入式负荷监测的问题模型及基本框架;然后分别对非侵入式负荷监测的数据采集与预处理过程、负荷分解模型与方法、常用数据集及评估指标进行归纳总结;最后,对目前研究中存在的挑战进行分析,并对未来的研究方向进行展望.","container-title":"自动化学报","DOI":"10/gjtf5r","ISSN":"0254-4156","language":"中文","note":"<北大核心,EI,CSCD>","page":"1-21","source":"CNKI","title":"非侵入式负荷监测综述","author":[{"family":"邓","given":"晓平"},{"family":"张","given":"桂青"},{"family":"魏","given":"庆来"},{"family":"彭","given":"伟"},{"family":"李","given":"成栋"}]}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[30]。事件是指用电设备在启停或运行状态改变时导致总能耗发生变化的现象。通过监视某一时间范围内的负荷变化来判断事件是否发生的方法，称之为事件检测，该方法可被划分为启发式模型、匹配滤波模型与概率模型ADDINZOTERO_ITEMCSL_CITATION{"citationID":"CmErKhhW","properties":{"formattedCitation":"\\super[30,32]\\nosupersub{}","plainCitation":"[30,32]","noteIndex":0},"citationItems":[{"id":551,"uris":["/users/7647885/items/8ZKL99GM"],"uri":["/users/7647885/items/8ZKL99GM"],"itemData":{"id":551,"type":"article-journal","abstract":"非侵入式负荷监测通过对总负荷电表数据进行分析处理,能够实现对各个用电设备及其工作状态的辨识,可广泛应用于建筑节能、智慧城市、智能电网等领域.近年来,随着智能电表的大规模部署以及各类机器学习算法的广泛应用,非侵入式负荷监测引起了学术界与工业界的共同关注.本文对非侵入式负荷监测方面的研究进行综述.首先提炼非侵入式负荷监测的问题模型及基本框架;然后分别对非侵入式负荷监测的数据采集与预处理过程、负荷分解模型与方法、常用数据集及评估指标进行归纳总结;最后,对目前研究中存在的挑战进行分析,并对未来的研究方向进行展望.","container-title":"自动化学报","DOI":"10/gjtf5r","ISSN":"0254-4156","language":"中文","note":"<北大核心,EI,CSCD>","page":"1-21","source":"CNKI","title":"非侵入式负荷监测综述","author":[{"family":"邓","given":"晓平"},{"family":"张","given":"桂青"},{"family":"魏","given":"庆来"},{"family":"彭","given":"伟"},{"family":"李","given":"成栋"}]},"label":"page"},{"id":923,"uris":["/users/7647885/items/G39D6MWB"],"uri":["/users/7647885/items/G39D6MWB"],"itemData":{"id":923,"type":"paper-conference","abstract":"MonitoringelectricityconsumptioninthehomeisanimportantwaytohelpreduceenergyusageandNon-IntrusiveLoadMonitoring(NILM)techniquesareapromisingapproachtoobtainestimatesoftheelectricalpowerconsumptionofindividualappliancesfromaggregatemeasurementsofvoltageand/orcurrentinthedistributionsystem.Inthispaper,wediscusseventdetectionalgorithmsusedintheNILMliteratureandproposenewmetricsforevaluatingthem.Inparticular,weintroducemetricsthatincorporateinformationcontainedinthepowersignalinsteadofstrictdetectionrates.WeshowthatthisinformationisimportantforNILMapplicationswiththegoalofimprovingapplianceenergydisaggregation.Ourworkwascarriedoutonapublicly-availableweek-longdatasetofrealresidentialpowerusage.","container-title":"IECON2012-38thAnnualConferenceonIEEEIndustrialElectronicsSociety","DOI":"10/gj2sk5","event":"IECON2012-38thAnnualConferenceofIEEEIndustrialElectronics","event-place":"Montreal,QC,Canada","ISBN":"978-1-4673-2421-2","language":"en","page":"3312-3317","publisher":"IEEE","publisher-place":"Montreal,QC,Canada","source":"DOI.org(Crossref)","title":"EventdetectionforNonIntrusiveloadmonitoring","URL":"/document/6389367/","author":[{"family":"Anderson","given":"KyleD."},{"family":"Berges","given":"MarioE."},{"family":"Ocneanu","given":"Adrian"},{"family":"Benitez","given":"Diego"},{"family":"Moura","given":"JoseM.F."}],"accessed":{"date-parts":[["2021",5,14]]},"issued":{"date-parts":[["2012",10]]}},"label":"page"}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[30,32]。启发式模型利用幅值变化等简单规则检测事件是否发生；匹配滤波模型ADDINZOTERO_ITEMCSL_CITATION{"citationID":"doJcMXSH","properties":{"formattedCitation":"\\super[33]\\nosupersub{}","plainCitation":"[33]","noteIndex":0},"citationItems":[{"id":926,"uris":["/users/7647885/items/G7C88VF2"],"uri":["/users/7647885/items/G7C88VF2"],"itemData":{"id":926,"type":"article-journal","abstract":"Themassivedeploymentofsmartmetersandothercustomizedmetershasmotivatedthedevelopmentofnonintrusiveloadmonitoring(NILM)systems.Thisistheprocessofdisaggregatingthetotalenergyconsumptioninabuildingintoindividualelectricalloadsusingasingle-pointsensor.Mostliteratureisorientedtoenergysaving.Nevertheless,activityofdailylivingsmonitoringthroughNILMisrecentlyreceivingmuchinterest.Thisproposalpresentsanevent-basedNILMalgorithmofhighperfor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过渡过程的波形等，往往可以采用傅里叶变换、小波变换ADDINZOTERO_ITEMCSL_CITATION{"citationID":"SERcaTdV","properties":{"formattedCitation":"\\super[37]\\nosupersub{}","plainCitation":"[37]","noteIndex":0},"citationItems":[{"id":937,"uris":["/users/7647885/items/KV5SZDSR"],"uri":["/users/7647885/items/KV5SZDSR"],"itemData":{"id":937,"type":"article-journal","abstract":"Thispaperpresentsanewapproachbasedonsemi-supervisedmachinelearningandwaveletdesignappliedtonon-intrusiveloadmonitoring.Co-trainingoftwomachinelearningclassifiersisusedtoautomatetheprocessoflearningtheloadpatternafterdesigningnewwavelets.Thenumericalresultsdemonstratingtheeffectivenessoftheproposedapproacharediscussedandconclusionsaredrawn.","container-title":"IEEETransactionsonSmartGrid","DOI":"10/gj25rp","ISSN":"1949-3053,1949-3061","issue":"6","journalAbbreviation":"IEEETrans.SmartGrid","language":"en","page":"2648-2655","source":"DOI.org(Crossref)","title":"Non-IntrusiveLoadMonitoringUsingSemi-SupervisedMachineLearningandWaveletDesign","volume":"8","author":[{"family":"Gillis","given":"JessieM."},{"family":"Morsi","given":"WalidG."}],"issued":{"date-parts":[["2017",11]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[37]等技术间接获得。在提取到负荷特征后，通过设计某种算法或学习策略构建负荷识别模型，以提取到的负荷特征作为输入，输出设备的状态、负荷种类等信息。现阶段，负荷识别方法主要包括有监督的与无监督的。有监督的负荷识别方法在获取总负荷数据的基础上，还需要某些状态标签数据进行模型训练，基于有监督学习算法的负荷识别模型主要有k近邻、贝叶斯方法、支持向量机、人工神经网络等ADDINZOTERO_ITEMCSL_CITATION{"citationID":"IcnWCMfS","properties":{"formattedCitation":"\\super[30,31]\\nosupersub{}","plainCitation":"[30,31]","noteIndex":0},"citationItems":[{"id":551,"uris":["/users/7647885/items/8ZKL99GM"],"uri":["/users/7647885/items/8ZKL99GM"],"itemData":{"id":551,"type":"article-journal","abstract":"非侵入式负荷监测通过对总负荷电表数据进行分析处理,能够实现对各个用电设备及其工作状态的辨识,可广泛应用于建筑节能、智慧城市、智能电网等领域.近年来,随着智能电表的大规模部署以及各类机器学习算法的广泛应用,非侵入式负荷监测引起了学术界与工业界的共同关注.本文对非侵入式负荷监测方面的研究进行综述.首先提炼非侵入式负荷监测的问题模型及基本框架;然后分别对非侵入式负荷监测的数据采集与预处理过程、负荷分解模型与方法、常用数据集及评估指标进行归纳总结;最后,对目前研究中存在的挑战进行分析,并对未来的研究方向进行展望.","container-title":"自动化学报","DOI":"10/gjtf5r","ISSN":"0254-4156","language":"中文","note":"<北大核心,EI,CSCD>","page":"1-21","source":"CNKI","title":"非侵入式负荷监测综述","author":[{"family":"邓","given":"晓平"},{"family":"张","given":"桂青"},{"family":"魏","given":"庆来"},{"family":"彭","given":"伟"},{"family":"李","given":"成栋"}]},"label":"page"},{"id":170,"uris":["/users/7647885/items/YDI939Z2"],"uri":["/users/7647885/items/YDI939Z2"],"itemData":{"id":170,"type":"thesis","abstract":"随着国家电网提出了建设泛在电力物联网的战略目标,用户内部设备的信息的感知和获取是提供智慧新业务的基础,而非侵入式负荷监测技术则是可以实现这一目的合适的智能感知方案。非侵入式负荷监测指的是将在用户总进线处获得的功耗聚合数据分解为各个电器设备运行功耗的技术。本文研究了两种不同类型的非侵入式负荷监测算法;基于事件的和基于状态的。针对于基于事件的非侵入式负荷监测算法的四个环节（事件检测、特征提取、负荷识别和能耗分解）提出了基于机器学习的不同的算法,并且都通过场景实验和公开数据集BLUED和ECO验证了算法的有效性。另外,本文还以隐马尔可夫模型为例研究了基于状态的非侵入式负荷监测,以探索两种不同类型方法的优劣势以及适用情况。整篇文章的工作具体由以下三部分组成。首先,本文针对基于事件的非侵入式负荷监测的事件检测环节提出了一种混合事件检测方法。它利用了多个不同方法的事件检测器以解决噪声对于事件检测的干扰。经过算例和实验分析,它比单一的事件检测方法在复杂的场景具有更好的检测效果。其次,在基于事件方法的负荷识别和能耗分解的环节中,本文分别提出了一种基于DBSCAN的无监督负荷识别方法和一种基于运行时间窗口的能耗分解方法。基于DBSCAN的无监督负荷识别方法在聚类的过程引入了对噪声的考虑,并将设备划分为周期型设备与用户驱动型设备。而基于运行时间窗口的能耗分解方法用以解决修复在能耗分解中被遗漏检测或是错误识别的事件产生不良影响。这两个算法的有效性都通过在公开数据集下得到了验证。最后,本文以隐马尔可夫模型为例,研究了基于状态的非侵入式负荷检测。通过实验,比较了它与基于事件的方法在不同采集频率下的监测效果,分析出它们分别适用的场景。","genre":"硕士","language":"中文;","note":"DOI:10.27461/ki.gzjdx.2020.000897","publisher":"浙江大学","source":"CNKI","title":"基于机器学习的非侵入式负荷监测技术研究","URL":"/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFD202002&filename=1020768289.nh&v=efIoR01DQIrDWNIQBYTsfUmIgB%25mmd2FhAqAW0CAGyW9x5xv39%25mmd2FaqlDNcaLQZcPZrNe8M","author":[{"family":"徐","given":"志翔"}],"accessed":{"date-parts":[["2021",3,14]]},"issued":{"date-parts":[["2020"]]}},"label":"page"}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[30,31]；无监督的负荷识别方法直接从数据中提取特征相似性，不需要标签数据训练模型，基于无监督学习算法的负荷识别模型主要包括K-meansADDINZOTERO_ITEMCSL_CITATION{"citationID":"1fvLTFRi","properties":{"formattedCitation":"\\super[38]\\nosupersub{}","plainCitation":"[38]","noteIndex":0},"citationItems":[{"id":939,"uris":["/users/7647885/items/CS73B7CV"],"uri":["/users/7647885/items/CS73B7CV"],"itemData":{"id":939,"type":"article-journal","container-title":"EnergyEfficiency","DOI":"10/f95v96","ISSN":"1570-646X,1570-6478","issue":"2","journalAbbreviation":"EnergyEfficiency","language":"en","page":"359-382","source":"DOI.org(Crossref)","title":"Influencingfactorsinenergyuseofhousingblocks:anewmethodology,basedonclusteringandenergysimulations,fordecisionmakinginenergyrefurbishmentprojects","title-short":"Influencingfactorsinenergyuseofhousingblocks","volume":"10","author":[{"family":"Cipriano","given":"X."},{"family":"Vellido","given":"A."},{"family":"Cipriano","given":"J."},{"family":"Martí-Herrero","given":"J."},{"family":"Danov","given":"S."}],"issued":{"date-parts":[["2017",4]]}}}],"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[38]、Mean-Shift、DBSCAN等聚类方法。综上所述，基于事件的用电负荷特性分析技术已经发展得较为成熟，准确性也相对较高，从数据采集到负荷识别的每个步骤均有大量研究人员进行方法研究。基于状态的用电负荷特性分析技术研究现状基于状态的用电负荷特性分析技术以设备状态作为目标变量，利用模式识别来求解关于各个设备功耗和与实际功耗差值最小的优化问题，从而推断出关于设备状态的最优解ADDINZOTERO_ITEMCSL_CITATION{"citationID":"lZk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