Power Quality Sensor for Smart Appliance’s Self-Diagnosing Functionality

Medina-Gracia, Ricardo; Gil-de-Castro, Aurora; Garrido-Zafra, Joaquín; Moreno-Munoz, A.; Cañete-Carmona, Eduardo

doi:10.1109/jsen.2019.2924574

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Thus, first, the importance of detailed energy metering must be emphasized. Submetering as opposed to bulk metering, involves measuring the energy consumption of individual units or appliances in the building [9]. Submeters would provide crucial information for more granular measurement of energy consumption data.…”

Section: Position and Work In Progressmentioning

confidence: 99%

Integration of CCHP microgrids in NZEB with critical loads under high PQR requirements, a position paper

et al. 2023

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Section: Position and Work In Progressmentioning

confidence: 99%

Integration of CCHP microgrids in NZEB with critical loads under high PQR requirements, a position paper

et al. 2023

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“…However, current research mainly focuses on human-computer interaction, promoting the adoption of SHs through industrial design and user experience (Kolanur et al, 2021;Medina-Gracia et al, 2019). Moreover, it mainly explores the influencing factors of intention to use SHs from consumer psychology in consumer behavior.…”

Section: Introductionmentioning

confidence: 99%

“…However, current research mainly focuses on human-computer interaction, promoting the adoption of SHs through industrial design and user experience (Kolanur et al. , 2021; Medina-Gracia et al. , 2019).…”

Section: Introductionmentioning

confidence: 99%

Do family factors affect the usage intention of smart homes? A PLS-SEM empirical study from China

Cui,

Tang,

Hassan

2024

APJML

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PurposeDespite the enormous benefits, smart homes (SHs) are still not widely adopted by residents in China. Furthermore, research on the intention to use SHs has overlooked the role of family factors. Thus, this study aims to propose a new research model to examine the impact of family factors on the usage intention (UI) of SHs.Design/methodology/approachThis study collected 265 valid data from potential users of SHs in China using a convenience sampling method. The PLS-SEM method was applied to test the research model and related hypotheses.FindingsThe empirical results confirm the mediating role of optimism (OP) in perceived family support (PFS)/perceived family trust (PFT) and attitude (AT). Unsurprisingly, the results validated the relationship between perceived usefulness (PU), attitude (AT) and UI of SHs through TAM theory. Besides, this study also identified the moderating effect of perceived risk (PR) between AT and UI.Practical implicationsTo improve SH adoption, practitioners should focus on family factors and utilize family influence to promote the spread of smart home reputation. Besides, SH practitioners should enhance user trust and reduce perceived risks through technological upgrades and security measures.Originality/valueBased on the Social Impact Theory and Technology Acceptance Model (TAM), this study is an empirical attempt to explore the impact of family factors on the intention to use SHs, expanding the research on smart home adoption.

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“…In the actual power system, with the new development of the internet of things (IoT) platform and the deployment of large number of edge devices, the need to collect data on the edge devices of IoT and make prediction at the edge becomes more urgent [16]. This requires us to develop methods with fewer parameters and a lower computational cost to apply to resource‐constrained devices [17, 18].…”

Section: Introductionmentioning

confidence: 99%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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This paper presents a new concise deep learning-based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre-processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non-linearity and long-term memory, while enabling the simple gated recurrent network model to be superior to Long Short-Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource-constrained internet of things (IoT) micro-controllers.

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Power Quality Sensor for Smart Appliance’s Self-Diagnosing Functionality

Cited by 12 publications

References 26 publications

Integration of CCHP microgrids in NZEB with critical loads under high PQR requirements, a position paper

Integration of CCHP microgrids in NZEB with critical loads under high PQR requirements, a position paper

Do family factors affect the usage intention of smart homes? A PLS-SEM empirical study from China

A simple gated recurrent network for detection of power quality disturbances

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