Forecasting Electric Vehicle charging demand using Support Vector Machines

Xydas, Erotokritos; Marmaras, Charalampos; Cipcigan, Liana; Hassan, Abubakar Sani; Jenkins, Nicholas

doi:10.1109/upec.2013.6714942

Cited by 59 publications

(26 citation statements)

References 17 publications

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“…Compared with private AGs, the installation cost per charger at the public AGs is lower, since aggregated EVs are charged through the same transformer instead of connecting EVs to different transformer as in the private case [65]. Considering the limited controllability of private AGs, we mainly discuss Trace-based model [71], [72] Data mining [73]- [75] Synthetic model MCS [76] Queueing model [77] Fluid dynamic traffic [78] Traffic assignment [79]- [81] Simulator-Based Model Commercial software [82]- [87] the public AG deployment in this section, which has its unique challenges:…”

Section: A Deployment Challengesmentioning

confidence: 99%

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Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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Section: A Deployment Challengesmentioning

confidence: 99%

“…Cluster and relational analysis is used first to classify the traffic pattern, then identify influential factors to analyze historical data in South Korea. Another effective method is to use support vector machines (SVM) for more time-sensitive traffic distribution [75].…”

Section: B Ev Travel Pattern Analysismentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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“…For constraint (8), in each time interval, those EVs whose departure time is approaching, as in Equation (13), are treated as controlled EVs that are full-power charging:…”

Section: Power-altering Charging Controlmentioning

confidence: 99%

Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

et al. 2016

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Abstract:With the ongoing large-scale implementation of electric vehicles (EVs), the exploration of a more flexible approach to maintain fair interaction between EVs and the power grid is urgently required. This paper presents an aggregator-based interactive charging management scheme adopting interruptible load (IL) pricing, in which the EV aggregator will respond to the load control command of the grid in an EV interactive mode. Charging managements are carried out according to battery state-of-charge and the EV departure time in EV charging stations. A power-altering charging (PAC) control method is proposed to dispatch the EVs charging fairly in a station and guarantee EV owners' preferences. The method does not require classical iterative procedures or heavy computations; furthermore, it is beneficial for EVs to depart earlier than expected for reasons beyond keeping homeostatic charging. The proposed scheme, which is tested to charge individual EVs well according to its preference, was implemented as part of an "EV Beijing" project. The proposed management scheme provides new insight into EV charging strategy and provides another choice to EV users.

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“…For example, Haldenbilen and Ceylan developed three energy demand models (GATEDE) based on the GA to improve transport energy demand estimation efficiency. Support vector machine (SVM) is another machine learning algorithm which is widely used in the field of forecasting the energy demand . Sun et al.…”

Section: Introductionmentioning

confidence: 99%

Use of group method of data handling for transport energy demand modeling

Teng

Xiao

et al. 2017

Energy Science & Engineering

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As transport sector takes a big share of the whole energy consumption in China, it is crucial to predict its energy demand. To forecast China's transport energy demand, group method of data handling (GMDH) was introduced. The model can help policymakers' select influential variables and build prediction models automatically. Furthermore, it can reduce the negative impact of the noise in the Chinese statistical data. To produce comparable results, four of the six data sets used in this paper contain the same variables as in previously published research. Artificial neural networks (ANN), GMDH, multiple linear regression (MLR), and support vector machine (SVM) models were trained using fivefold cross-validation. The performance of these models was measured in terms of coefficient of determination and root mean square error. Results showed that GMDH achieved better performance than the other models. Finally, projections were made with two scenarios. Both of the projected results showed that the energy demands peak in certain years and then decrease gradually. This study suggests that GDP is not the essential variable, while urbanization rate is an important variable to forecast the transport energy demand in China. It also suggests that Chinese government needs to prepare for the development and deployment of transport energy. 303

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Forecasting Electric Vehicle charging demand using Support Vector Machines

Cited by 59 publications

References 17 publications

Energy and Information Management of Electric Vehicular Network: A Survey

Energy and Information Management of Electric Vehicular Network: A Survey

Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

Use of group method of data handling for transport energy demand modeling

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