TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring

Cheng, Xu; Zhao, Meng; Zhang, Jianhua; Wang, Jinghao; Pan, Xin; Liu, Xiufeng

doi:10.1109/hdis56859.2022.9991439

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…Although in the inception of the energy disaggregation framework, CO was the method initially proposed by Hart [55], traditionally, it is the FHMM approach that has been widely used in the literature. Recent progress in the machine learning field and especially deep learning, however, has paved the way for the introduction of advanced techniques, such as LSTM, decision trees, and more recently convolution neural networks (CNN) with promising results [62,63] and numerous improvements. The focus of this research is neither to capture the nuances in the latest developments of energy disaggregation algorithms nor to propose a new algorithm altogether.…”

Section: Energy Disaggregation Algorithmsmentioning

confidence: 99%

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

Koasidis,

Marinakis,

Doukas

et al. 2023

Energies

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Energy behaviours will play a key role in decarbonising the building sector but require the provision of tailored insights to assist occupants to reduce their energy use. Energy disaggregation has been proposed to provide such information on the appliance level without needing a smart meter plugged in to each load. However, the use of public datasets with pre-collected data employed for energy disaggregation is associated with limitations regarding its compatibility with random households, while gathering data on the ground still requires extensive, and hitherto under-deployed, equipment and time commitments. Going beyond these two approaches, here, we propose a novel data acquisition protocol based on multiplexing appliances’ signals to create an artificial database for energy disaggregation implementations tailored to each household and dedicated to performing under conditions of time and equipment constraints, requiring that only one smart meter be used and for less than a day. In a case study of a Greek household, we train and compare four common algorithms based on the data gathered through this protocol and perform two tests: an out-of-sample test in the artificially multiplexed signal, and an external test to predict the household’s appliances’ operation based on the time series of a real total consumption signal. We find accurate monitoring of the operation and the power consumption level of high-power appliances, while in low-power appliances the operation is still found to be followed accurately but is also associated with some incorrect triggers. These insights attest to the efficacy of the protocol and its ability to produce meaningful tips for changing energy behaviours even under constraints, while in said conditions, we also find that long short-term memory neural networks consistently outperform all other algorithms, with decision trees closely following.

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Section: Energy Disaggregation Algorithmsmentioning

confidence: 99%

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

Koasidis,

Marinakis,

Doukas

et al. 2023

Energies

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show abstract

“…The performance of supervisors and operators may deteriorate at the same time, which will weaken the effect of operation supervision [20]. In recent years, the energy industry has begun to introduce various computer intelligent decision-making methods to improve the safety [21][22][23][24]. Computer intelligent supervision technology can make use of the advantages of massive memory, fast calculation, and never fatigue to make up for the deficiencies of operators and prevent human error.…”

Section: Introductionmentioning

confidence: 99%

An Operation Scheme Generation Method for Nuclear Power Plant Operation under the Condition of No Operating Procedures Guided

et al. 2023

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Large-scale, complex, and high-risk industrial systems such as nuclear power plants have developed detailed operating procedures. Under the expected conditions, the operators operate the system according to the operating procedures to avoid human error. However, under complex and unfamiliar conditions, once the guidance of operating procedures is lost, serious consequences may be caused. This paper proposes a No-Procedure guided Operation Supervision Technology (NoP-OST). The key idea is to identify the success paths by a forward-searching approach from source to sink functions. On this basis, the success paths are combined to generate various operation schemes to achieve the main objectives of the system. The paper illustrates the application of NoP-OST through the case of loss of the Residual Heat Removal System (RHRS) in a Pressurized Water Reactor (PWR) nuclear power plant. The research results show that the method proposed in this paper can provide a valuable operational reference for complex systems by making use of the available functions of the system. In the future, edge computing will be needed to optimize this method for achieving the best output power and efficiency, while ensuring the security, stability, and reliability of the system.

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TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring

Cited by 2 publications

References 39 publications

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring

An Operation Scheme Generation Method for Nuclear Power Plant Operation under the Condition of No Operating Procedures Guided

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