Location Analysis of Urban Electric Vehicle Charging Metro-Stations Based on Clustering and Queuing Theory Model

Chen, Chongyu; Li, Teng; Wang, Shugui; Hua, Zhenyao; Kang, Zecheng; Li, Dongyang; Guo, Weian

doi:10.1007/978-981-19-1253-5_21

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…An overview of the clustering method used in [21] for charging station clustering is shown in Figure 17. Similarly, Chen et al [78] employed the K-means clustering technique to compute the number of charging stations for EVs and their locations.…”

Section: K-means Clusteringmentioning

confidence: 99%

“…Finding prospective recharging station locations [21] Number of charging stations for EVs and the location of charging stations [78] Spectral clustering and the Gaussian Mixture Model Optimal charging station locations for EVs [50] Agglomerative hierarchical approach Different levels of clusters for charging stations [79] Fuzzy C-means clustering method Optimal location of charging stations [44] Coordinated clustering algorithms Optimal candidates for charging stations [80]…”

Section: K-means Algorithmmentioning

confidence: 99%

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Applications of Clustering Methods for Different Aspects of Electric Vehicles

2023

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The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.

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Section: K-means Clusteringmentioning

confidence: 99%

Section: K-means Algorithmmentioning

confidence: 99%

Applications of Clustering Methods for Different Aspects of Electric Vehicles

2023

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“…It is a BP neural network structure diagram composed of N nodes in the input layer, M nodes in the hidden layer, and P nodes in the output layer-see Figure 3. Literature [22] proposed a BP neural network prediction model based on a simulated particle swarm algorithm. The experimental results show that the optimization algorithm has a good pre-calibration effect for network parameter optimization.…”

Section: Application Of Bp(back Propagation) Neural Network In Extrem...mentioning

confidence: 99%

Survey on the Application of Deep Learning in Extreme Weather Prediction

et al. 2021

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Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework.

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Location Analysis of Urban Electric Vehicle Charging Metro-Stations Based on Clustering and Queuing Theory Model

Cited by 2 publications

References 12 publications

Applications of Clustering Methods for Different Aspects of Electric Vehicles

Applications of Clustering Methods for Different Aspects of Electric Vehicles

Survey on the Application of Deep Learning in Extreme Weather Prediction

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