NILM Application for Real Time Monitoring of Appliances Energy Consumption Used

Dowalla, Krzysztof; Bilski, Piotr; Łukaszewski, Robert; Wójcik, Augustyn; Kowalik, Ryszard

doi:10.1109/idaacs53288.2021.9660389

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…A pioneering work introduced in 1992 by G. Hart [16] was based on a proposal to change the methods of constructing and identifying admittance-based residential loads. Specifically, the author advanced NILM as a technique which involves several processes: a) data acquisition [17], b) feature extraction [6], c) event detection [18], d) load identification [19] and e) energy disaggregation [20]. From among the works which discuss NILM, the research in [16] represents the current reference for Internet-of-Things (IoT) development in both domestic and industrial applications, especially the construction industry, even though the paper is more than 30 years old.…”

Section: A Recent Nilm Approaches For Industrial and Domestic Environ...mentioning

confidence: 99%

“…In [17], the authors presented an application which shows the consumer, in real-time, the energy consumption of each of their appliances. The main drawback of the proposed approach is that it requires two measuring stations to sample appliance behavior at frequencies from several dozen Hz to 1 MHz whereas a laboratory setup is able to monitor 24 sockets with connected appliances and provide more effective disaggregation.…”

Section: A Recent Nilm Approaches For Industrial and Domestic Environ...mentioning

confidence: 99%

“…All the images created for training and validation are available from [42]. To compare with the literature, we refer to the works which studied Best MLP [6], Two-Stage Detection [18], Deep DMFL [5], Hart [16] and Dowalla [17]. The results for Best MLP indicate good accuracy (best case of 95%), but a dedicated, advanced, and expensive measurement node is required, and the usable flash memory has some limitations.…”

Section: Denardo Appliance Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Load Monitoring and Appliance Recognition Using an Inexpensive, Low-Frequency, Data-to-Image, Neural Network, and Network Mobility Approach for Domestic IoT Systems

Fazio,

Mehic,

Voznak

2024

IEEE Internet Things J.

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With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real-time and based on the Non-Intrusive Load Monitoring (NILM) paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own dataset, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study's simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

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