Semi-Automatic Generation and Labeling of Training Data for Non-intrusive Load Monitoring

Völker, Benjamin; Scholl, Philipp M.; Becker, Bernd

doi:10.1145/3307772.3328295

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…In other words, events can be assumed to be anomalies in the signal, which makes it possible to utilize a range of known methods for their detection [41]. In practice, event detection algorithms span the range from computationally lightweight solutions (e.g., using thresholds between successive power samples [39,50,51]) to the application of probabilistic models and voting methods [52][53][54]. More recently, the application of even more complex filters to electrical signals was proposed in order to suppress minor fluctuations while emphasizing actual events.…”

Section: Extracting Higher-level Informationmentioning

confidence: 99%

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: Extracting Higher-level Informationmentioning

confidence: 99%

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

et al. 2021

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“…Aggregated data are recorded using a custom-built measurement system referenced as the SmartMeter from now on. The system was introduced by Völker et al in [23,24]. A schematic wiring diagram of the smart meter can be seen in Figure 2.…”

Section: Smart Metermentioning

confidence: 99%

“…PowerMeters are custom-built smart plugs designed to measure current and voltage waveforms of individual appliances. The plugs were introduced by Völker et al in [22,23]. Their general system architecture is nearly identical to the architecture of the SmartMeter.…”

Section: Distributed Metersmentioning

confidence: 99%

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A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

et al. 2020

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In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days.

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“…Nevertheless, NILMPEds is also suitable to benchmark high-and low-frequency event detection algorithms (e.g., [15]). Furthermore, the different detection models can be used to test data labeling platforms [16][17][18], as well as to study the potential impacts of event detection algorithms in smart-grid applications that require this information (e.g., appliance activation forecast for smart-charging of electric vehicles and battery energy storage systems).…”

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NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring

Pereira

2019

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Datasets are important for researchers to build models and test how these perform, as well as to reproduce research experiments from others. This data paper presents the NILM Performance Evaluation dataset (NILMPEds), which is aimed primarily at research reproducibility in the field of Non-intrusive load monitoring. This initial release of NILMPEds is dedicated to event detection algorithms and is comprised of ground-truth data for four test datasets, the specification of 47,950 event detection models, the power events returned by each model in the four test datasets, and the performance of each individual model according to 31 performance metrics. SummaryPublic datasets are crucial elements in data science research as these not only allow researchers to perform systematic evaluations and benchmarks of their algorithms but also enable other researchers to replicate and reproduce existing research [1].Non-Intrusive Load Monitoring (NILM or load disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electrical distribution of a building [2]. A typical NILM dataset is a collection of electrical energy measurements, taken from the mains (i.e., aggregate consumption) and from the individual loads (i.e., ground-truth data, which are obtained either by measuring each load at the plug-level or measuring the circuit to which the load is connected [3].As presented in a recent review [3], there are over 20 public datasets for NILM research. According to the same review, these datasets can be categorized according to their suitability to be used to evaluate event-based and event-less approaches [4]. Event-based strategies seek to disaggregate the total consumption employing detecting and labeling appliance transition (referred to as power events) in the aggregated signal. As such, datasets for event-based NILM must also include labels for the power events from the appliances of interest. On the other hand, event-less approaches attempt to match each sample of the aggregated power to the consumption of one specific device, or a combination of different devices. Therefore, datasets for event-less approaches do not require any labeled transitions.Existing NILM datasets are also categorized according to the data reporting rates [5]: macroscopic datasets with data reporting rates around 1 Hz, and microscopic datasets with rates of several kHz.

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Semi-Automatic Generation and Labeling of Training Data for Non-intrusive Load Monitoring

Cited by 12 publications

References 17 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

A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring

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