Non-intrusive load monitoring under residential solar power influx

Dinesh, Chinthaka; Welikala, Shirantha; Liyanage, Yasitha; Ekanayake, M. P. B.; Godaliyadda, G. M. R. I.; Ekanayake, Janaka

doi:10.1016/j.apenergy.2017.08.094

Cited by 50 publications

(33 citation statements)

References 52 publications

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“…However, the possibility of their future use is much wider. For example, their adaptation to issues related to the detection of anomalies in the functioning of electricity receivers before the total failure or subsequent destruction of the technical infrastructure takes place should be considered (see also [19]).…”

Section: Resultsmentioning

confidence: 99%

Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)

Jasiński

2019

MATEC Web Conf.

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The paper addresses the issue of modelling the demand for electricity at the level of residential buildings with the use of artificial intelligence tools, namely artificial neural networks (ANN). The real data for six buildings acquired by measurement meters installed in them was used in the research. Their original frequency of 1 Hz has been resampled to a frequency of 1/600 Hz which corresponds to a period of 10 minutes. Out-of-sample forecasts verified the ability of ANN to disaggregate electricity usage for its specific applications. Four categories were distinguished, which were electricity consumption by: (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 approach were used. The simulations included a total of over 10,000 ANNs differing, e.g. architecture, input variables, activation functions, their parameters, and training algorithms. The research confirmed the possibility of using ANN in modelling the disaggregation of electricity consumption and indicated the way of building a highly optimized model.

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Section: Resultsmentioning

confidence: 99%

Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)

Jasiński

2019

MATEC Web Conf.

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“…There are few researchers that have focused on state recognition within multi-state appliances. 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.…”

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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“…Prior literature [11,12,31] has made a great effort and reviewed many features to give insights about feature engineering in NILM. The most widely used features of appliances include active power, reactive power and on/off durations, especially for those low-frequency sampling methods [13,16,[18][19][20][21][22]24,26,27,29,36]. The major drawback of those three features is that different types of appliances may consume the same active power and they behave similarly, in which case these features cannot work.…”

Section: Features and Features Additive Propertymentioning

confidence: 99%

“…Both methods assume that the appliances' energy usage is always nonnegative, neglecting the existence of distributed photovoltaic and wind power systems. In fact, Dinesh has considered the solar power influx which consumes negative active power [22]. Nevertheless, Dinesh and colleagues utilized a subspace component power level matching algorithm which needs optimization over every subspace component and poses high computational requirement.…”

Section: Introductionmentioning

confidence: 99%

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

2018

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Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances' feature is added to their belonging network, an equal amount of change in the network's feature can be observed) of three features (harmonic feature, voltage-current trajectory feature, and active-reactive-distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage-Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on-off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

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Non-intrusive load monitoring under residential solar power influx

Cited by 50 publications

References 52 publications

Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)

Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)

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

A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances

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