Supervised classification of distributed data streams for smart grids

Guarracino, Mario Rosario; Irpino, Antonio; Radziukynienė, Neringa; Verde, Rosanna

doi:10.1007/s12667-012-0049-x

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…BD algorithms are arising in ML given the immense amount of data collected daily, which makes its processing very difficult by using standard methods. Its usage is not only necessary in some cases, but also beneficial (e.g., in the case of distributed computing, different models could be created using spatial local information, and a more general model could be considered to be mixing the local models, as done in [112]). BD approaches usually involve a data partitioning step.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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“…This can be seen as an advance with respect to the measurement process, but without employing the concept of machine learning for taking decisions autonomously. In other words, the smart meter does not prevent the fraud, but only provides information more quickly [2], [26], [27].…”

Section: B Smart Grid and Its Relation To Commercial Lossesmentioning

confidence: 99%

“…The concept of "Smart Grid", using smart meters, allows the integration of electrical equipment with data communication networks in a managed and automated system by the electric utility, making the energy to be supplied with safety, reliability and efficiency. Therefore, a network can enable [2], [26], [27]:…”

Section: B Smart Grid and Its Relation To Commercial Lossesmentioning

confidence: 99%

“…The concept of Smart Grid has increased with the demand growth for automatic reading of energy meters. Beyond the aim of reducing frauds, thefts of energy and faulty measurements, several electric utilities have been concerned to better characterize the profile of consumers with irregularities, and also to correctly identify illegal connections [26], [27]. Therefore, minimizing commercial losses may guarantee investments in programs for power quality, and may allow a reduction of the price to the consumer.…”

Section: B Smart Grid and Its Relation To Commercial Lossesmentioning

confidence: 99%

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On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

Ramos

Rodrigues

Souza

et al. 2018

IEEE Trans. Smart Grid

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According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$ 4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization. Index Terms-Commercial losses, black hole algorithm, optimum-path forest. I. INTRODUCTION T HE ENERGY losses are defined as the difference between the energy generated or purchased and the energy billed, being classified in two different types: technical and commercial losses. The former are related to the physical characteristics of the energy system, i.e., the technical losses are defined as the energy lost in the transportation, transformation and in the measuring equipments, being its costs predicted by the electric utilities [1]. The commercial losses, also called non-technical losses (NTL), are associated with the energy delivered to the consumer that is not billed, being more difficult to be detected and quantified.

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“…Also in this case the states can be seen as directly observable. SVM is proposed for anomaly detection of distributed data streams in the smart grid . Anomalies can be attacks to the smart grid and load unbalance, which ultimately can cause faults and blackouts in the electric power system.…”

Section: Introductionmentioning

confidence: 99%

Modeling electricity load curves with hidden Markov models for demand-side management status estimation

Andersson

2016

Int. Trans. Electr. Energ. Syst.

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Summary The development of the smart grid will lead to increased possibilities to improve energy efficiency via demand‐side management (DSM) and ultimately contribute to a more sustainable and secure energy system. The smart grid will offer great opportunities for developing methods for automatic analyses of various states in the energy system. This paper proposes a method that aims to classify load curves and estimate current power reduction capacity from DSM. The method is based on the hidden Markov model where the hidden states represent customers' demand behaviors in an aggregated load curve. The method is tested on 3 aggregated load curves, based on measured data from 36 Swedish companies. Results show that the method can recognize when DSM has been utilized as well when DSM has not been utilized. Results also show that the energy system becomes more stable, from a system dynamical point of view, when DSM is utilized.

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Supervised classification of distributed data streams for smart grids

Cited by 6 publications

References 12 publications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

A Review of Classification Problems and Algorithms in Renewable Energy Applications

On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

Modeling electricity load curves with hidden Markov models for demand-side management status estimation

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