A non-intrusive load monitoring system using multi-label classification approach

Buddhahai, Bundit; Wongseree, Waranyu; Rakkwamsuk, Pattana

doi:10.1016/j.scs.2018.02.002

Cited by 45 publications

(18 citation statements)

References 23 publications

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“…Local and Global Consistency (LGC), Gaussian Fields and Harmonic Functions(GFHF), and Manifold Regularization (MR)). Another multi-label classification of appliances using RAndom k-labELsets (RAkEL) with Decision Tree (DT) was explored in [7]. However, it was observed that the lowpower consumption appliances were not correctly identified.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

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Section: Related Workmentioning

confidence: 99%

“…A disaggregation model can be trained either as a singletarget [5], [14] or multi-target [15] regression problem, or as a single-label [2], [16]- [18], or multi-label [3], [7], [19]- [22] classification problem. Single-and multi-label classification and multi-class classification were explored in many works in the literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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“…Using the convolution kernel, results are generated in the form of a map of features. In the CNN, in addition to convolution layers, there are typically such layers as: (i) pooling-most often using the maximum or average function (selecting the maximal or calculating the average value from the data area with the dimensions of the filter used)-to reduce the spatial amount of input data [32], (ii) flatten-transforming a two-dimensional dataset into a vector (one-dimensional data) enabling it to be sent to (iii) a dense layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…For this reason, only selected categories of electricity receivers with a sufficient number of plug meter readings (presented in Table 2) were analysed. The data were divided into three subsets: training, validation, and test (a similar approach was used by Buddhahai et al [32]). Figure 8 shows an example of the arithmetic average of plug meter readings (with a measurement frequency of 1 Hz) recorded in ten minutes.…”

Section: Selection and Preparation Of Input And Output Variables Of Mmentioning

confidence: 99%

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Jasiński

2020

Energies

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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.

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“…Several studies have demonstrated that multi-label learning represents a viable alternative to conventional NILM approaches [23][24][25][26][27]. For example, the work by [25], investigated the possibility of applying a temporal multi-label classification approach in non-event based NILM where a novel set of meta-features was proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Faustine

Pereira²

2020

Energies

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The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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A non-intrusive load monitoring system using multi-label classification approach

Cited by 45 publications

References 23 publications

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

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