An Appliance Load Disaggregation Scheme Using Automatic State Detection Enabled Enhanced Integer Programming

Dash, Shitikantha; Sodhi, Ranjana; Sodhi, Balwinder

doi:10.1109/tii.2020.2975810

Cited by 18 publications

(16 citation statements)

References 25 publications

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“…Table 6 shows the high accuracy of our classification method for each appliance in comparison with the results of [24,34]. Keeping in mind that a higher number of appliances should diminishes the accuracy of NILM, note that the number of appliances considered in this work and in [24] are seven and six, respectively. On the other hand, considering multi-mode appliances such as dishwasher increases the complexity of disaggregation.…”

Section: Classificationmentioning

confidence: 89%

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Event Matching Classification Method for Non-Intrusive Load Monitoring

2021

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Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

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Section: Classificationmentioning

confidence: 89%

“…However, their main drawback is the need for an enormous training dataset that is not in general feasible to collect [23]. Therefore, extracting useful information from a small training dataset for the NILM classification problem has become a topic of great interest in the past few years [24].…”

Section: Related Workmentioning

confidence: 99%

Event Matching Classification Method for Non-Intrusive Load Monitoring

2021

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“…[34] proposed optimization-based approaches for energy disaggregation of the main electrical appliances of residences. In [33] the authors applied quadratic programming; in [34] the authors applied integer programming. Both used ultra low frequency data (10 to 15 min resolution) and their results were poor compared to studies that use low and high frequency data.…”

Section: Related Studiesmentioning

confidence: 99%

“…Considering appliance identification and load disaggrega-tion, different techniques have been proposed in literature, such as: Discrete Fourier Transform [12]; Decision trees [13]; Principal Component Analysis (PCA) [14], [15], Genetic Programming [16], Artificial Neural Networks (ANN) [17]- [19], Deep Artificial Neural Networks (DNN) [20]- [26], Hidden Markov Models (HMM) [27]- [31], Integer and Quadratic Programming [32]- [34], Transfer learning [23], among others. Many of these techniques make use of several consumption parameters obtained from data collected at high frequency, which require expensive meters, that are not viable for residential use.…”

Section: Introductionmentioning

confidence: 99%

Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration

Carmo

Castro

2020

IEEE Access

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Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system.

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“…Many studies have implemented residential load identification (Dash et al, 2021;Kong et al, 2016;D'Incecco et al, 2020;Chen et al, 2020;Zhou et al, 2021) based on two characteristics of residential electricity consumption, namely, high regularity and a limited number of household appliances. These studies mainly adopt the non-mechanistic methods based on publicly available datasets (Kolter and Johnson, 2011;Anderson et al, 2012;Kahl et al, 2016;Parson et al, 2016;Monacchi et al, 2014;Kelly and Knottenbelt, 20152015), which means that the identification features do not come from the electric principle of equipment but from the information provided by public datasets.…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Motor Load Identification Method Based on Load Transient Features

2022

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Motor load accounts for more than 50% of the total electric power load in China. Identifying the load of induction motors non-intrusively is of great importance for the design of energy-saving schemes and formulation of demand-side response strategies in industrial enterprises. Based on the transient mechanism of the induction motor, the present work first defines some motor load start-up transient feature parameters with clear physical meanings and proposes a set of non-intrusive motor load identification methods applicable to industrial settings. In addition, a case study that applied the proposed method to the industrial setting was performed to verify its effectiveness. The results showed that the proposed method can overcome the problem of misidentification caused by the fact that the start-up transient process is affected by its mechanical load characteristics and hence can identify motors with similar running power and has good anti-interference capacity despite power quality disturbances.

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An Appliance Load Disaggregation Scheme Using Automatic State Detection Enabled Enhanced Integer Programming

Cited by 18 publications

References 25 publications

Event Matching Classification Method for Non-Intrusive Load Monitoring

Event Matching Classification Method for Non-Intrusive Load Monitoring

Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration

A Non-Intrusive Motor Load Identification Method Based on Load Transient Features

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