Data mining for energy systems: Review and prospect

Liu, Wenxuan; Zhao, Junhua; Wang, Dianhui

doi:10.1002/widm.1406

Cited by 4 publications

(2 citation statements)

References 73 publications

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“…So, the rationale behind this work is, to exploit information that may arise from large databases. This procedure applies in practice all key stages of the extracting knowledge process from large databases [13,14].…”

Section: Introductionmentioning

confidence: 99%

Energy cluster analysis based on consumption data in different weather condition

Kazolis,

Kogias,

Roumeliotis

2023

E3S Web Conf.

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The main aim of this effort is the discovery of knowledge from data, concerning consumption of electric energy, during the year 2022, based on unattended learning methods. These data were collected from the Public Electricity Company of Kavala and the methods used are, at first the Factor analysis and second the K-means clustering algorithm. The overhead methodologies are realized by the use of Statistica Data Miner software.

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Section: Introductionmentioning

confidence: 99%

Energy cluster analysis based on consumption data in different weather condition

Kazolis,

Kogias,

Roumeliotis

2023

E3S Web Conf.

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“…Another challenge is what to do with the large volume of data (bigdata) that is generated. (W. Liu et. al.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis of Literature on the Social Benefits and Advantages of The Adoption of Intelligent Electric Energy Meters

Santos

Silva

Souza³

et al. 2022

Proceedings IX Simpósio Brasileiro De Sistemas Elétricos

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This article presents a systematic review of the literature on the use of smart meters. The current approach has shown a focus on data acquisition and processing and, mainly, on automation, which are certainly important factors. However, the non-technical approach has been little explored. This condition leads to reflection on aspects beyond the technical scope, such as social, environmental and economic. This work contributes to broadening the vision of researchers in the field of smart grids, presenting the main aspects of the works that the authors' knowledge until now, emphasizing the lack of investigations beyond the purely technical nature, leading to the conclusion that there is still much to explore in the social field through the implementation of smart grids

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Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Barkhi,

Poorhossein,

Hosseini

2024

Sci Rep

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Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available measurements using RMS values of symmetrical components. The learning-based characteristic of the suggested technique with a low computational burden exempts the need for an available communication infrastructure in the MG. The Support Vector Machine (SVM) technique is applied to train the Intelligent Electronic Devices to have a reliable MG protection scheme. The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. Digital simulations on an MV test network were conducted to construct an appropriate database to consider some aspects of uncertainty in the network. The various faults considering their uncertainty, the different modes of operation, the uncertainty of RESs generation, and the load levels are combined to produce myriad scenarios. The simulation results confirm the effectiveness of the proposed adaptive protection approach in accurately distinguishing different system modes and consistently protecting the MG, achieving an accuracy rate of 99.75%. Furthermore, it offers the MG an optimal protection scheme that is not limited by selectivity constraints across diverse conditions.

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Data mining for energy systems: Review and prospect

Cited by 4 publications

References 73 publications

Energy cluster analysis based on consumption data in different weather condition

Energy cluster analysis based on consumption data in different weather condition

Systematic Analysis of Literature on the Social Benefits and Advantages of The Adoption of Intelligent Electric Energy Meters

Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

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