Relevance vector machine with optimal hybrid kernel function for electric vehicles ownership forecasting: The case of China

Bao, Haibo; Guo, Xiaoxuan; Liang, Junjie; Lan, Fei; Li, Jiangwei; Chen, Guangsheng; Mo, Jiangting

doi:10.1016/j.egyr.2022.02.132

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…Type II-EVs drive from a non-RR to a non-RR; c. Type III-EVs drive from a non-RR to a RR. The duration of the above three types of driving processes follows lognormal distributions (4).…”

Section: The Temporal Characteristics Analysis Of Travelsmentioning

confidence: 99%

“…Accurate forecasting of EV load is the premise of optimal planning of CSSs. The literature on the subject mostly focuses on the PEV load forecasting [1–22]. Some approaches aim at providing the medium‐long‐term forecasting of EV load, for example, the overall load change of PEVs in different years [1–4].…”

Section: Introductionmentioning

confidence: 99%

“…The literature on the subject mostly focuses on the PEV load forecasting [1–22]. Some approaches aim at providing the medium‐long‐term forecasting of EV load, for example, the overall load change of PEVs in different years [1–4]. Deep learning method are used to obtain PEV load curves [5–12].…”

Section: Introductionmentioning

confidence: 99%

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Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Zhu

Borghetti

et al. 2023

IET Generation Trans & Dist

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Electric vehicles (EVs) charging swapping stations (CSSs), as well as multi‐functional integrated charging and swapping facilities (CSFs), have become important to reduce the impact of e‐mobility on the electric power distribution system. This paper presents a coordinated planning optimization strategy for CSSs/CSFs and active distribution networks (AND) that includes distributed generation. The approach is based on the application of a specifically developed spatial‐temporal load forecasting method of both plug‐in EVs (PEVs) and swapping EVs (SEVs). The approach is formulated as a mathematical programming optimization model that provides the location and sizing of new CSSs, the best active distribution network topology, the required distributed generation, and substation capacities. The developed model is solved using CPLEX, and its characteristics and performances are evaluated through a realistic case study.

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“…Type II-EVs drive from a non-RR to a non-RR; c. Type III-EVs drive from a non-RR to a RR. The duration of the above three types of driving processes follows lognormal distributions (4).…”

Section: The Temporal Characteristics Analysis Of Travelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Zhu

Borghetti

et al. 2023

IET Generation Trans & Dist

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“…As the automotive industry advances, the number of motor vehicles domestically has seen a rapid surge, reaching 412 million by September 2022, with cars comprising 76.5% of this total [1,2]. This represents a year-on-year increase of 4.3% compared to the 395 million recorded in 2021.…”

Section: Introductionmentioning

confidence: 99%

Improved Attention Mechanism for Human-like Intelligent Vehicle Trajectory Prediction

Shen,

Xiao,

et al. 2023

Electronics

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In order to overcome the low long-term predictive accuracy associated with mainstream prediction models and the limited consideration of driver characteristics, this study presents an enhanced attention mechanism for human-like trajectory prediction, which is based on Long Short-Term Memory (LSTM). A novel database structure is proposed that incorporates data about driving style and driving intent, pertaining to human factors. By utilizing the convolution computation of Convolutional Social-Long Short-Term Memory (CS-LSTM) for surrounding vehicles, spatial feature extraction around the target vehicle is achieved. Simultaneously, we introduce a dynamic driving style recognition model and a human-like driving intent recognition model to fulfill the output of the human-like module. From a temporal perspective, we employ a decoder attention mechanism to reinforce the emphasis on key historical information, while refining the attention mechanism based on driving style for human-like weight assignment. Comparative analysis with other models indicates that the proposed Driving Style-based Attention-enhanced Convolutional Social-Long Short-Term Memory (DACS-LSTM) model exhibits notable advantages in predicting human-like trajectories for long-term tasks. Visualizing the predicted trajectories of both the Attention-enhanced Convolutional Social-Long Short-Term Memory (ACS-LSTM) and our proposed model, and analyzing the impact of the human-like module on the predicted trajectory, shows that our model’s predicted trajectory aligns more closely with the actual one. By comparing the weight distribution of the conventional attention mechanism and the enhanced attention mechanism proposed here, and analyzing the trajectory changes in conjunction with the driving styles, it becomes evident that our proposed model offers a marked improvement.

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“…Relevant researchers have carried out a forecast analysis of the vehicle population in China. Based on the Relevance Vector Machine, Bao et al (2022) predicted the number of electric vehicles in China, is expected to exceed 20 million by 2030. Using actual sales data from 2010 to 2018, Rietmann et al (2020) predicted China's electric vehicle inventory through a logistics growth model, the results show that the electric vehicle market share will reach 50.9% in 2035. believe that by 2050, the sales of BEVs in China may reach 30.4-64.6% and PHEV technology will be favoured by consumers between 2020 and 2030.…”

Section: Introductionmentioning

confidence: 99%

Contribution of potential clean trucks in carbon peak pathway of road freight based on scenario analysis: A case study of China

Zhang

Liu

et al. 2022

Journal of Cleaner Production

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Relevance vector machine with optimal hybrid kernel function for electric vehicles ownership forecasting: The case of China

Cited by 14 publications

References 14 publications

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Coordinated planning of charging swapping stations and active distribution network based on EV spatial‐temporal load forecasting

Improved Attention Mechanism for Human-like Intelligent Vehicle Trajectory Prediction

Contribution of potential clean trucks in carbon peak pathway of road freight based on scenario analysis: A case study of China

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