Electric Load Disaggregation in Smart Metering Using a Novel Feature Extraction Method and Supervised Classification

Chahine, Khaled; Drissi, Khalil El Khamlichi; Pasquier, Christophe; Kerroum, Kamal; Faure, Claire; Jouannet, Thierry; Michou, Michel

doi:10.1016/j.egypro.2011.05.072

Cited by 31 publications

(11 citation statements)

References 4 publications

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“…Thus, more supervised learning is used in NILM like Support Vector Machines (SVM), k-Nearest Neighbor (k-NN), and clustering methods such as k-means. The k-NN method was applied [10][11][12][13][14][15]. The k-NN method was used to identify five common electrical appliances [10].…”

Section: Introductionmentioning

confidence: 99%

“…The k-NN method was used to identify five common electrical appliances [10]. Chahine proposed a new feature extraction scheme to build a data set, and then used it to train k-NN [11]. Rahimi used the real power and reactive power as features in k-NN for describing the load-signatures of individual devices, which achieved great accuracy [13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

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Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Gao

Jiao

2019

Processes

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“…Bayesian and Hidden Markov Model techniques are being used in a variety of smart metering applications such as load disaggregation [42], appliance identification [43] and supply demand analysis [44]. Future applications will result in a broader range of needs which will see more and more methods applied and tailored for smart metering to bring out greater benefits.…”

Section: B Tools For Smart Meteringmentioning

confidence: 98%

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon

2016

IEEE Trans. Ind. Inf.

477

170

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Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics which deal with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of metering process, the different stakeholder interests and technologies used to satisfy stakeholder interests. Furthermore the paper highlights the challenges as well as opportunities arising due to the advent of big data and the increasing popularity of the cloud environments.time pricing schemes, and demand-response for energy saving and efficient use of energy generated.The research and development in smart meters and their applications have been progressed rapidly in recent years and many methods and techniques have been developed. The technological scope relating to smart meters covers a diverse range of ICT technologies, such as electronics and communication, and there have already been several surveys done, e.g.[7] looks into the different applications of smart grids focusing on the communication needs and also on communication requirements of smart meters for integration into power grids.[8] is a comprehensive survey of communication technologies for smart meters which could be used to satisfy the identified needs.[9] introduces a novel smart meter communication technology, [10] examines the web and data service aspect of smart meter networks, and [11] proposes a framework for smart meter privacy. In this paper, we examine these developments from a holistic data analytic viewpoint. We will also outline potential future applications and challenges that lie ahead. As a foundation for our holistic approach the key components of electricity meter intelligence is shown as in Figure 1. The three key components capture the aspects of data, technology and stakeholders. These aspects and the role they play in smart meter intelligence is discussed in section 2.

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“…The result is a compact representation of the current in terms of complex numbers referred to as poles and residues [5,6]. These complex numbers are shown to be characteristic of the considered load and thus can serve as features for the subsequent classification phase [7]. For both synthetic and real data, results indicate that poles and residues extracted by the MPM allow an almost perfect reconstruction of drawn electric currents.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring

Chahine

Drissi²

2013

Applied Computational Intelligence and Soft Computing

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Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change concerns of the present time. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on feature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues. These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated values of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution of the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a three-dimensional feature space with reduced intercluster overlap.

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Electric Load Disaggregation in Smart Metering Using a Novel Feature Extraction Method and Supervised Classification

Cited by 31 publications

References 4 publications

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring

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