EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Shen, Yi; Fang, Wei; Ye, Feng; Kadoch, Michel

doi:10.3390/electronics9010080

Cited by 34 publications

(21 citation statements)

References 33 publications

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“…The authors did not provide evaluation for clustering, but the labels generated by clustering were then used by ANN to classify user behavior. A similar approach was noted in [75] where K-means clustering was used to find 3 clusters of charging behavior using Euclidean distance measure. The cluster evaluation was not performed but the results were used by K-NN algorithm for classification and the accuracy of classification was 97.9 with area under ROC curve (AUC) value of 0.994.…”

Section: Unsupervised and Statistical Learning For Analysis And Pmentioning

confidence: 99%

“…Cluster evaluation not provided. [75] Find cluster of EV charging behavior to provide labels for each cluster, then use classification for future sessions to predict which cluster it belongs to [90] to classify charging profiles of EV. A charging profile can simply be considered as the distribution of charging arrival and departure times of EVs.…”

Section: Deep Learning For Analysis and Prediction Of Charging Behmentioning

confidence: 99%

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Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Unsupervised and Statistical Learning For Analysis And Pmentioning

confidence: 99%

Section: Deep Learning For Analysis and Prediction Of Charging Behmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…Shen et al 20 discuss about V2G (vehicle to grid) used with EV and IoT. The proposed architecture improves cost effectiveness and quality of services.…”

Section: Methodsmentioning

confidence: 99%

Intelligent charging station in 5G environments: Challenges and perspectives

2021

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Summary The popularity of the electric vehicles has been growing due to the concern of increase in environmental pollution. In order to overcome the environmental issues, electric vehicles have been introduced all over the globe. As the use and number of electric vehicles is growing, there is a great demand to identify the sustainable solutions for intelligent charging station dedicated to electric vehicles. The objective of this paper is to explore the possibilities for intelligent charging station using 5G services. This paper is a state‐of‐the‐art concept to identify the current challenges and issues of intelligent charging station. It should be noted that in the future, the use of the smart charging station will dramatically improve the efficiency of current charging stations. It is identified based on several existing techniques that the biggest challenges are the connectivity between the vehicles and among different charging stations. This manuscript could be useful for researchers working on intelligent charging station worldwide and on their integration with the recent communication technologies like 5G. The features of intelligent charging stations combined with the fifth‐generation network represent a new path for humanity and introduce a safer and more advanced environment. From data privacy to human safety, the network provides enough resources to rise driving quality, time and fuel economy and human lives saving. The results of this research can be well applied in the industry or in the field of academic research.

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“…In addition, Smart neighborhoods aimed to start the connection between communities and enable a variety of mechanisms for managing energy. Similarly to a Home are network (HAN), a smart Neighborhood Area Network (NAN) are used to communicate and coordinate with smart homes (for example: each with its own HAN) [119]. NAN gathered data using smart meters installed in each smart home.…”

Section: F Smart Home Neighborhood Energy Managementmentioning

confidence: 99%

A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems

Sami

2020

IEEE Access

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The authors would like to thank the DSR technical and financial support. ABSTRACT I-energy and its applications used in the smart grid are chosen as attractive research issues due to their widespread applications in the smart home. In this vein, the optimization of energy demand in smart home appliances, in a smart grid, is considered the main challenge faced by power companies during peak periods regarding its considerable effect on the global power system stabilities. Therefore, this paper aims to show explicitly the performance of smart grid on electricity and hydrogen applications with reused communication methods. To facilitate the understanding of the communication-reused methods in the smart grid, several components are identified and presented in terms of improvements, benefits and lack of features. In the smart grid, most innovation communications are being used as wireline and wireless bundle, where the main features are aggregated. Indeed, reducing consumers' electricity bills in smart home electrical systems relies on increasing the relationship between the highest energy demand and annual energy use. The most appropriate research has been reviewed on optimization approaches discussing the smart home electric system. The examined methods are categorized into trustworthy and metaheuristic algorithms. INDEX TERMS Smart home, I-energy concept, hydrogen-renewable energy system, smart meter, smart grid, risk management, demand-side management, AMI.

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EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Cited by 34 publications

References 33 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Intelligent charging station in 5G environments: Challenges and perspectives

A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems

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