Research on EV loads clustering analysis method for source-grid-load system

Li, Yong; Yang, Gaofeng; Luo, Wei; Zhou, Yuqing; Hu, Runzi; Liu, Yuming; Zhan, Hang; Yu, Qingguang

doi:10.1016/j.egyr.2022.10.354

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…The charging behaviors of different brands and types of EVs have large differences, making it difficult to establish a unified forecasting model. Therefore, this section proposes an easy-to-implement method to cluster EVs and select the universal charging law of a typical type of EV to approximate the simulation of the charging behavior of a single vehicle under that type [7].…”

Section: Ev-type Clustering Considering Charging Behaviorsmentioning

confidence: 99%

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

Yi,

Ren

2023

J. Phys.: Conf. Ser.

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The traditional electric vehicle (EV) load forecasting methods are mostly used to predict large areas such as cities, which makes it difficult to serve practical applications. The existing charging station load forecasting methods ignore the traveling and charging behaviors of EVs. To this end, a charging station ultra-short-term load prediction method considering the traveling and charging behaviors of EVs is proposed. Firstly, EV types are classified according to charging behaviors. On this basis, a rolling forecast model of the number of arrivals per unit time of each vehicle type is established, and then the impact of charging randomness on load forecast is reduced by repeated Monte Carlo sampling. The total load of charging stations during the prediction period can be obtained by adding up the load of individual vehicles. The results show that the method enables accurate prediction of ultra-short-term load at charging stations, which has higher accuracy and reference value than the traditional load forecasting methods.

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Section: Ev-type Clustering Considering Charging Behaviorsmentioning

confidence: 99%

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

Yi,

Ren

2023

J. Phys.: Conf. Ser.

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“…The literature [13] uses clustering to characterize driver behavior patterns, and then uses the clustered data to train a machine learning-based classifier to provide a solution for identifying the characteristics of electric vehicles and their driver behavior. The literature [14] compared the DBI performance of charging post data under K-means clustering, direct clustering, and DPC clustering, respectively, to analyze several typical EV load behaviors and evaluate their tunable characteristics.…”

Section: Introductionmentioning

confidence: 99%

Integrated profile of EV user behavior and value based on charging data mining

Zhu,

Hu,

Yan

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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This study propose an integrated user behavior and value profiling method, which aims to build a more comprehensive index system to evaluate user value and demand. This method mainly includes a Time-Decay-Based Label Matching (TDBLM) method and an extended RFM model. The TDBLM method can effectively deal with the multi-label problem of user charging records due to clustering, and can derive a unique label that can better characterize the current state of users; the extended RFM model is used for user value profiles, and the experimental results show that user value profiles are defined into four categories: Lost, Potential, Retention, and High-value. In order to evaluate the performance of K-means++ used in this method, its performance was compared with that of GMM and Mean Shift clustering methods, and the results showed that the clustering results of K-means++ have better stability and are more suitable for user profiling. Finally, this study also proposes a method to visualize user profiles and make the profile results as knowledge graphs to realize the visualization and fast retrieval of profiles and expand the profile function.

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“…At present, there are many methods about clustering analysis, such as systematic clustering method, hierarchical clustering method, fuzzy clustering method and so on (Li. et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

et al. 2023

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Since the discovery of the Carboniferous gas reservoirs in East Sichuan in 1977, after more than 40 years of development, most of the gas reservoirs have entered the middle and late stages of development. The gas reservoir is characterized by strong heterogeneity, large difference in permeability, and serious impact of water invasion in some blocks. Therefore, how to make a correct decision on gas field development and deployment is of vital importance. Combined with system clustering, BP neural network, correlation analysis and other methods, this paper first analyzes and calculates the static indicators of the Carboniferous gas reservoirs, and then divides the gas reservoirs into four categories using ward clustering method according to the calculated weight value, and determines the characteristics of each type of gas reservoirs using correlation coefficient analysis method. Finally, the recovery prediction model of each type of gas reservoir is established according to the BP neural network. The results indicate that: (1) The recovery rate prediction model can predict the trend of cumulative gas production changes, thereby obtaining the space for improving recovery rate, and the accuracy of the prediction results is high, which can be used as a reference for gas field planning. (2) The sub-active gas reservoirs with strong heterogeneous water bodies and the inactive gas reservoirs with low permeability water bodies have a certain space for enhanced oil recovery.

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Research on EV loads clustering analysis method for source-grid-load system

Cited by 7 publications

References 1 publication

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

Integrated profile of EV user behavior and value based on charging data mining

Cluster analysis of carboniferous gas reservoirs and application of recovery prediction model

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