Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method

Niu, Geng; Ji, Yu; Zhang, Zhihui; Wang, Wenbo; Chen, Jikai; Yu, Pengfei

doi:10.1016/j.egyr.2021.08.049

Cited by 26 publications

(17 citation statements)

References 5 publications

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“…This iterative process persists until no more items are left to designate as new clusters. The primary objective of the algorithm is to optimize the grouping of items into clusters, refining the centroids with each iteration (Niu et al, 2021;Sinan et al, 2023;Wang et al, 2023).…”

Section: K-means Clustering Methodsmentioning

confidence: 99%

K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

Ridzki,

Hadijah,

Mukidin

et al. 2023

IJSSR

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This study aims to utilize the K-means clustering algorithm in data mining to categorize sales data at XYZ Grocery store. The research is essential for understanding sales patterns and enhancing inventory management strategies. The research methodology involves implementing the K-means clustering algorithm to generate centroid values for each cluster, thereby creating groups of products based on their sales performance. The findings of this study are expected to provide insights into sales trends at the store. While the abstract provides a general overview, specific results and contributions of this research are not detailed. Further studies could offer a more in-depth understanding of the practical applications of these findings in improving store management and inventory control.

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Section: K-means Clustering Methodsmentioning

confidence: 99%

K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

Ridzki,

Hadijah,

Mukidin

et al. 2023

IJSSR

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“…Clustering-based techniques streamline load forecasting by grouping similar load patterns, which aids in managing the variability from different energy sources and consumer behaviors. Techniques like K-means, hierarchical clustering, combined locally linear embedding (LLE), principal component analysis (PCA), and multi-layer perceptrons (MLPs), enhance accuracy, integrate renewable energy sources (RESs), aid demandside management (DSM), and bolster SG functions [15,[43][44][45][46]80]. Time series load forecasting (TSLF) methods utilize historical data, enhanced by advanced algorithms such as ARIMA and neural networks, to predict future demand.…”

Section: Comprehensive Approaches To Forecastingmentioning

confidence: 99%

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Aghahadi,

Bosisio,

Merlo

et al. 2024

Applied Sciences

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This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems.

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“…The main idea of this algorithm is to define one K-center for each cluster, the cluster center must be randomly selected [22]. Euclidean distance can be found using Equation 1 [23].…”

Section: K-meansmentioning

confidence: 99%

Implementation of Naïve Bayes Classifier for Classifying Alzheimer’s Disease Using the K-Means Clustering Data Sharing Technique

Putri,

Hastari,

Faizah

et al. 2023

PREDATECS

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Alzheimer's disease is a neurodegenerative disease that is very universal and characterized by memory loss and cognitive function decline which ultimately leads to dementia. In 2015, it is estimated that around million people worldwide will suffer from Alzheimer's disease or dementia. Globally, the number of Alzheimer's diseases will increase from 26.6 million in 2006 to 106.8 million cases in 2050. Due to the large number of people with Alzheimer's disease, it is necessary to classify symptoms that lead to indicators of Alzheimer's disease, so that data mining methods are used for data processing. Alzheimer's data taken from Kaggle amounted to 373 records, through the stages of data preprocessing, data sharing using the Hold-Out method and clustering with AK-Means algorithm. The data is processed using data mining techniques using NBC algorithms. Validation testing the accuracy value obtained the result that the NBC algorithm with K-Means Clustering data sharing has relatively better accuracy than the hold-Out method of 91.89%.

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Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method

Cited by 26 publications

References 5 publications

K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

K-Means Algorithm Method for Clustering Best-Selling Product Data at XYZ Grocery Stores

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

Implementation of Naïve Bayes Classifier for Classifying Alzheimer’s Disease Using the K-Means Clustering Data Sharing Technique

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