State identification of home appliance with transient features in residential buildings

Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.     

An error correction framework for sequences resulting from known state-transition models in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.     

基于非侵入式负荷监测的用户行为精细化分析

随着非侵入式电力负荷监测的发展,如何利用用户负荷用电细节信息,分析用户用电行为特征成为重要的研究方向。本文提出了基于非侵入式负荷监测的用户行为精细化分析方法,根据历史数据建立用户行为特性指标,提出基于因子分析与支持向量机的用户聚类方法。在此基础上,建立基于精细化负荷数据的用户需求响应潜力评估指标。算例结果表明,所研究内容可以准确分析用户行为,为电力公司施行需求侧管理提供科学指导。     

计及非侵入式负荷监测及负荷预测的设备运行模式研究

提出了一种新型非侵入式负载监控(Non-Intrusive Load Monitoring,NILM)方法,该方法结合了设备使用模式(Appliance Usage Patterns,AUP)以提高主动负载识别和预测的性能。在第一阶段,使用基于频谱分解的标准NILM算法来学习给定AUP;利用所得AUP通过专门构建的模糊系统来获得设备的先验概率。AUP基于最近的设备活动/不活动和一天中的时间,给出了每个设备在当前时刻处于活动状态的可能性度量。因此,通过AUP确定的先验概率增加了NILM算法的有效负载识别精度。将所提方法应用于几组实际家庭数据库,证明了其对主动负载估计的改进能力。利用所提方法制定并实施了住宅用电量预测机制,实验结果证明了所提方法的有效性。     

基于设备运行状态检测与能量回归同步评估的居民非介入式负荷辨识算法研究

非侵入负荷辨识技术能够高效低成本地获得用户分项电能并支撑多种业务,基于分项电器能量回归的神经网络为负荷辨识技术提供了重要支撑。文中针对神经网络进行能量分解时在设备关停处的噪声识别污染及基于能量阈值法评估设备运行状态的局限性,提出了基于设备能量分解与运行状态分类的硬参数共享多任务学习模型,并根据能量回归与状态识别对输入序列全局与区域信息的敏感度差异,提出基于多感受野融合的时间卷积网络,实验结果表明文中所提算法模型在辨识效果上取得了提升,并在洗衣机、洗碗机等小功率波动设备上相较传统网络减少了50%的平均能量绝对误差。     

基于负荷分解技术的小区电能替代需求分析

电能替代不仅提高了居民家庭用能设备的电气化水平,同时减少了环境污染,符合社会可持续发展对能源消费的要求.提出一种基于负荷分解技术的比例模型,用于估计电能替代后小区总用电增量.首先分析用户用能场景,提出可用于电能替代的电器.基于公开数据集建立这类电器的负荷分解模型,并在此基础上为小区少量用户安装电流采集器获取低频电能数据...     

基于NILM技术的家庭用户精确负荷建模方法

结合非侵入负荷监测NILM系统的负荷辨识流程,本文提出了一种基于NILM技术的家庭用户精确负荷建模方法。该方法应用NILM技术提取家庭主要设备负荷特性。然后通过模糊C聚类法实现家庭负荷模型归类,获得设备针对不同电价的转移灵敏度和自灵敏度用电特性,并在此基础上形成家庭负荷特性。通过电网公司分时电价环境下实测的家庭典型用电负荷数据验证可知,空调、洗衣机、热水器、电动汽车具有较大的弹性,其中洗衣机的自弹性和交叉弹性最大,在高电价时段可削减100%。该方法所获得的家庭负荷辨识的结果,可支持居民电价/激励等需求侧管理政策的制定,也可支持用户家庭用电设备状态监测服务等。     

Challenges for real-world applications of nonintrusive load monitoring and opportunities for machine learning approaches

The rapid increase of power consumption calls for efficient and effective energy usage and conservation strategies in buildings. One of the requirements of achieving such a goal is load monitoring of residential appliances. Among the available load monitoring frameworks, nonintrusive load monitoring (NILM) which is used to estimate the appliance-level power usage from the aggregated signals from smart meters, has the potential to be widely deployed. This paper presents an up-to-date review of NILM methods and the challenges existing in each step of NILM. Then this paper reviews two state-of-the-art machine learning based NILM methods including Hidden Markov Model and Deep Learning techniques. Finally, this paper discusses areas for future research and development of NILM in real-world applications, where machine learning approaches can play a more significant and even decisive role.     

基于新型卷积神经网络的非侵入式负载监测方法

现有非侵入式负载监测技术处理负载规模变化的能力弱,且随着负载的种类复杂化与数量的增多,其具有估计精度不高的问题。建立了一种考虑用户用能多时间尺度耦合特性,并具有规模化处理能力的新型卷积神经网络,以提高复杂规模化负载估计的精确性。该神经网络包括多时间尺度感知与特征提取模块、自我关注模块和对抗损失模块等,多时间尺度感知与特征提取模块可获取与整合不同时间尺度负载数据的耦合特征,自我关注模块和对抗损失模块根据耦合特性来进一步提高监测模型的估计精度。最后,通过仿真分析验证了所提方法的有效性和优越性。     

Towards automated appliance recognition using an EMF sensor in NILM platforms

Non-Intrusive Load Monitoring (NILM) has been studied for a few decades now as a method of disaggregating information about appliance level power consumption in a building from aggregate measurements of voltage and/or current obtained at a centralized location in the electrical system. When such information is provided to the electricity consumer as feedback, they can then take the necessary steps to modify their behavior and conserve electricity. Research has shown potential for savings of up to 20% through this kind of feedback. The training phase required to allow the algorithms to recognize appliances in the home at the beginning of a NILM setup is a big hindrance to wide adoption of the technique. One of the recent advances in this research area includes the addition of an Electro-Magnetic Field (EMF) sensor that measures the electric and magnetic field nearby an appliance to detect its operational state. This information, when coupled with the aggregate power consumption data for the home, can help to train a NILM system, which is a significant step forward in automating the training phase. This paper explores the theory behind the operation of the EMF sensor and discusses the feasibility of automating the training and classification process using these devices. A case study is presented, where magnetic field measurements of eight appliances are analyzed to determine the viability of using these signals alone to determine the type of appliance that the EMF sensor has been placed next to. Various dimensionality reduction techniques are applied to the collected data, and the resulting feature vectors are used to train a variety of common machine learning classifiers. A vector subspace obtained using Independent Component Analysis (ICA), along with a k-NN classifier, was found to perform best among the different alternatives explored. Possible reasons behind the findings are discussed and areas for further exploration are proposed.