A Smart and Safe Electricity Consumption Model for Integrated Energy System Based on Electric Big Data

Liu, Qinzhe; Tong, Bingbing; Li, Dongliang; Lü, Yan; Fu, Yuhong; Chen, Lei; Zhao, Kuangyi

doi:10.18280/ijsse.100412

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Addressing high safety risks in the new integrated energy system requires sophisticated computations for safe and smart electricity consumption. K-means clustering is utilized to create a smart and safe electricity consumption model for the integrated energy system, leveraging big data management [204], [205], [206]. Using a risk-based clustering method to identify Near Misses among safe scenarios is important since the possibility of recovering the combinations of failures in a tolerable time allows to avoid deviations to accident, reducing the downtime and its risk to the system [207].…”

Section: ) Risk Assessmentmentioning

confidence: 99%

“…This clustering method enhances optimization modeling by providing both reliability and the capacity to maximize performance. Optimization is a transversal field that includes PV and wind generation predictions, EVs and load consumption forecasting, and other different kind of stochastic models [70], [89], [158], [204], [214], [264], [265], [266]. K-means clustering, combined with other algorithms, helps to provide reliable and optimized forecasting.…”

Section: H Optimizationmentioning

confidence: 99%

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K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: ) Risk Assessmentmentioning

confidence: 99%

Section: H Optimizationmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…Thanks to the progress in information technology, big data technology has been widely adopted by many industries. The reliability of the distribution system could be effectively enhanced, if big data management is integrated with the distribution system operation to mine the massive data collected by power detectors and sensors [18][19][20][21]. Through association analysis and clustering optimization, Wang et al [22] determined the relationship between historical states and fault features of devices, and established a multi-dimensional evaluation index system for reliability evaluation of the power system, based on the difference evaluation results of electronic devices.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation of Direct Current Distribution System for Intelligent Buildings Based on Big Data Analysis

2021

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In intelligent buildings, the power is distributed in the direct current (DC) mode, which is more energy-efficient than the traditional alternating current (AC) mode. However, the DC distribution system for intelligent buildings faces many problems, such as the stochasticity and intermittency of distributed generation, as well as the uncertain reliability of key supply and distribution devices. To solve these problems, this paper evaluates and predicts the reliability of the DC distribution system for intelligent buildings through big data analysis. Firstly, the authors identified the sources of the big data on DC distribution system for reliability analysis, and constructed a scientific evaluation index system. Then, association rules were mined from the original data on the evaluation indices with MapReduce, and a reliability evaluation model was established based on Bayesian network. Finally, the proposed model was proved valid through experiments. The research provides reference for reliability evaluation of the DC distribution system in various fields.

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A Smart and Safe Electricity Consumption Model for Integrated Energy System Based on Electric Big Data

Cited by 2 publications

References 16 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Reliability Evaluation of Direct Current Distribution System for Intelligent Buildings Based on Big Data Analysis

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