Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

Bousbiat, Hafsa; Klemenjak, Christoph; Leitner, Gerhard; Elmenreich, Wilfried

doi:10.1109/i2mtc43012.2020.9128406

Cited by 16 publications

(15 citation statements)

References 18 publications

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“…Even though detecting anomalies in smart meter data is challenging, signal processing and machine learning techniques can efficiently be utilized for this purpose. For example, detecting anomalies in smart meter data can be used to enable Ambient Assisted Living (AAL), where consumption patterns are indicative of the Activities of Daily Livings (ADLs) executed by the residents [98][99][100][101]. Detecting unusually short or long ADLs, or unexpected ADLs sequences, in general, are often suitable indicators of unusual user behavior.…”

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

“…Clement et al [98] presented a semi-Markov model that describes the daily use of appliances to detect human activity/behavior from smart meter data. In [99], smart meter data are analyzed to identify the behavioral patterns of the occupants, and Bousbiat et al [100] proposed a framework for detecting abnormal ADLs from smart meter data.…”

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

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Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

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Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

Section: Recognizing Patterns and Anomaliesmentioning

confidence: 99%

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

et al. 2021

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“…Further approaches exist that use NILM as a Home Energy Management System (HEMS) to compensate peak loads and, thus, ensure grid stability (e.g., charging the batteries of electric cars primarily in off-peak times) [ 2 , 11 ], to detect appliance malfunction [ 1 , 11 ], to detect energy theft [ 1 ], or for HAR in the AAL domain [ 1 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Wilhelm

Kasbauer

2021

Sensors

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Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.

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“…We have also introduced the concept of user activities, which is straightforward terms refer to actions that people perform involving the use of electric appliances, e.g., doing the laundry (involves clothes washer, clothes dryer, and iron) or preparing a meal (oven, stove, microwave, aid choppers, and blenders). Such user activities are important to evaluate different NILM application domains, e.g., Non-Intrusive Activity Detection (NIAD) 27 , and the detection of abnormal consumption behaviors 28 . It is important to note that one individual appliance activity can only be associated with one user activity (cardinality of 0 or 1).…”

Section: Ground-truthmentioning

confidence: 99%

EMD-DF64: A 64-Bit File Format for Energy Monitoring and Disaggregation Datasets

Pereira¹,

Velosa²,

Pereira³

2021

Preprint

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There is a consensus in the Non-Intrusive Load Monitoring research community on the importance of public datasets in furthering load disaggregation research. Nevertheless, despite the considerable efforts to release public data, not many steps have been taken towards homogenizing how these are made available to the community and providing seamless access to the available and future datasets.In this paper, we present the Energy Monitoring and Disaggregation Data Format (EMD-DF64). EMD-DF64 is a data model, file format, and application programming interface developed to provide a unique interface to create, manage, and access electric energy datasets. The proposed format is an extension of the well-known Sony WAVE64 format that supports the storage of audio data and metadata annotations in the same file, thus reducing the number of artifacts that have to be maintained by the dataset users.

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Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

Cited by 16 publications

References 18 publications

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

EMD-DF64: A 64-Bit File Format for Energy Monitoring and Disaggregation Datasets

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