Electric vehicle charging demand forecasting model based on big data technologies

Arias, Mariz B.; Bae, Sungwoo

doi:10.1016/j.apenergy.2016.08.080

Cited by 249 publications

(97 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Considering that drivers' driving times and distances are constant, it is common to study the modeling of the EVs charging behaviors with fuel vehicle data. From the perspective of modeling data, these research data are derived from the national household travel survey (NHTS) data of America [11], daily traffic flow data of Jiangsu Province of China [10], and daily traffic flow data of South Korea [12]. Characteristic parameters of fuel vehicles should be converted into characteristic parameters of EVs charging via certain assumptions when the above data are applied in the actual study.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

2020

View full text Add to dashboard Cite

The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities. Therefore, an accurate joint modeling approach of the arrival time, the staying time, and the charging capacity for the EVs charging behaviors in the work area based on ternary symmetric kernel density estimation (KDE) is proposed in accordance with the actual data. First and foremost, a data transformation model is established by considering the boundary bias of the symmetric KDE in order to carry out normal transformation on distribution to be estimated from all kinds of dimensions to the utmost extent. Then, a ternary symmetric KDE model and an optimum bandwidth model are established to estimate the transformed data. Moreover, an estimation evaluation model is also built to transform simulated data that are generated on a certain scale with the Monte Carlo method by means of inverse transformation, so that the fitting level of the ternary symmetric KDE model can be estimated. According to simulation results, a higher fitting level can be achieved by the ternary symmetric KDE method proposed in this paper, in comparison to the joint estimation method based on the edge KDE and the ternary t-Copula function. Moreover, data transformation can effectively eliminate the boundary effect of symmetric KDE.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

2020

View full text Add to dashboard Cite

show abstract

“…BDA allow to identify new market trends and determine root causes of issues, failures, and defects. Data analytics can predict customers' preferences and needs by examining customer behavior, which can drive creativity and innovation in business services [48]. In one study, a model was presented to predict the electric vehicle charging demand that used weather data and historical real-world traffic data.…”

Section: Bda and Demand Planningmentioning

confidence: 99%

Big Data Analytics and Its Applications in Supply Chain Management

Darvazeh¹,

Vanani²,

Musolu³

2020

New Trends in the Use of Artificial Intelligence for the Industry 4.0

View full text Add to dashboard Cite

In today's competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. So, the main purpose of this book chapter is to explore the application of BDA in supply chain management (SCM).

show abstract

“…For each different period, different charging demand was revealed. (Arias & Bae, 2016) Fast charging stations were examined in morning, afternoon and evening as a part of feasibility study for the investment of installing energy storage systems and renewable energy sources. (Arias et al, 2017) The study on PEV users in Ireland shows that most of them charge their vehicles in the evening at home when the highest demand of electricity occurs.…”

Section: Introductionmentioning

confidence: 99%

Electrical Vehicles Charging Coordination by Fuzzy Logical System

Kiliçarslan¹,

Ateş²,

Aydilek³

et al. 2018

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

View full text Add to dashboard Cite

Today, with the rapid technological development, interest in electric vehicles is also increasing. This raises the question of what the effects of the vehicles on the electric power network will be. In this article, the adverse effects of charging scenarios of electric vehicles' batteries on the electric power network are examined and fuzzy logic based solutions are proposed to prevent or reduce the effects of charging electric vehicles during peak hours. In this article a fuzzy logic system that is providing the cheapest charging prices, also reducing the impact on the load curve of Turkey's electrical network is provided.

show abstract

Electric vehicle charging demand forecasting model based on big data technologies

Cited by 249 publications

References 20 publications

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation

Big Data Analytics and Its Applications in Supply Chain Management

Electrical Vehicles Charging Coordination by Fuzzy Logical System

Contact Info

Product

Resources

About