Pattern matrix and decision tree based technique for non-intrusive monitoring of home appliances

Choksi, Kushan Ajay; Jain, Sachin

doi:10.1109/icpes.2017.8387403

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Chinthaka Dinesh et al proposed a non-intrusive load monitoring method that simultaneously determines the amount of solar inflow and the device information (such as transient state, operation state, and level of power consumption based on the spectral clustering method), to automatically classify different operating modes of multi-state appliances [17]. Kushan Ajay Choksi et al [18] proposed a method of constructing a power matrix that converts different states to three different values of −1, 0, and +1, and then uses machine learning to realize the state recognition of electrical appliances. In order to improve the identification accuracy of multi-state electric appliances, this paper Energies 2020, 13, 792 3 of 12 proposes a method of state recognition responding to specific electrical equipment types based on power time series.…”

Section: Related Workmentioning

confidence: 99%

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

et al. 2020

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Non-intrusive load monitoring, a convenient way to discern the energy consumption of a house, has been studied extensively. However, most research works have been carried out based on a hypothetical condition that each electric appliance has only one running state. This leads to low identification accuracy for multi-state electric appliances. To deal with this problem, a method for identifying the type and state of electric appliances based on a power time series is proposed in this paper. First, to identify the type of appliance, a convolutional neural network model was constructed that incorporated residual modules. Then, a k-means clustering algorithm was applied to calculate the number of states of the appliance. Finally, in order to identify the states of the appliances, different k-means clustering models were established for different multi-state electric appliances. Experimental results show effectiveness of the proposed method in identifying both the type and the running state of electric appliances.

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Section: Related Workmentioning

confidence: 99%

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

et al. 2020

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“…The proposed methodology ensures that no intrusive metadata other than power consumption metering data is used. The data used has a sampling time of 15 min which is sufficiently large to avoid any type of appliance level segregation [25, 26]. Moreover, the knowledge of grid topology is also avoided.…”

Section: Related Work Research Gaps and Objectivesmentioning

confidence: 99%

Rule‐based classification of energy theft and anomalies in consumers load demand profile

2019

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The invent of advanced metering infrastructure (AMI) opens the door for a comprehensive analysis of consumers consumption patterns including energy theft studies, which were not possible beforehand. This study proposes a fraud detection methodology using data mining techniques such as hierarchical clustering and decision tree classification to identify abnormalities in consumer consumption patterns and further classify the abnormality type into the anomaly, fraud, high or low power consumption based on rule-based learning. The proposed algorithm uses real-time dataset of Nana Kajaliyala village, Gujarat, India. The focus has been on generalizing the algorithm for varied practical cases to make it adaptive towards nonmalicious changes in consumer profile. Simultaneously, this study proposes a novel validation technique used for validation, which utilizes predicted profiles to ensure accurate bifurcation between anomaly and theft targets. The result exhibits high detection ratio and low false-positive ratio due to the application of appropriate validation block. The proposed methodology is also investigated from point of view of privacy preservation and is found to be relatively secure owing to low-sampling rates, minimal usage of metadata and communication layer. The proposed algorithm has an edge over state-of-the-art theft detection algorithms in detection accuracy and robustness towards outliers.

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“…In recent years, non-intrusive power load monitoring and decomposition (NILMD) technology has attracted the attention of many scholars due to the high cost, low efficiency and limited application of traditional power load monitoring methods [1][2][3][4][5][6][7][8][9][10]. Har [1] initially put forward the idea and theory of non-invasive load decomposition, mainly through load decomposition at the entrance of residential electricity load.…”

Section: Introductionmentioning

confidence: 99%

“…Suzuki et al [5] used integer programming to decompose and identify electrical equipment. Choksi et al [6] proposed to identify electrical equipment based on power load characteristics and decision tree algorithm. Hassan et al [8] expands and evaluates appliance load signatures based on V-I trajectory-the mutual locus of instantaneous voltage and current waveforms, and they also demonstrate the use of variants of differential evolution as a novel strategy for selection of optimal load models.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive load monitoring and decomposition method based on decision tree

Jiang

Ding

et al. 2020

J.Math.Industry

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In order to realize the problems of non-intrusive load monitoring and decomposition (NILMD) from two aspects of load identification and load decomposition, based on the load characteristics of the database, this paper firstly analyzes and identifies the equipment composition of mixed electrical equipment group by using the load decision tree algorithm. Then, a 0-1 programming model for the equipment status identification is established, and the Particle Swarm Optimization (PSO) is used to solve the model for equipment state recognition, and the equipment operating state in the equipment group is identified. Finally, a simulation experiment is carried out for the partial data of Question A in the 6th "teddy cup" data mining challenge competition.

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Pattern matrix and decision tree based technique for non-intrusive monitoring of home appliances

Cited by 7 publications

References 17 publications

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

Rule‐based classification of energy theft and anomalies in consumers load demand profile

Non-intrusive load monitoring and decomposition method based on decision tree

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