scholarly journals Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (5) ◽  
pp. 1253 ◽  
Author(s):  
Yunyan Li ◽  
Yuansheng Huang ◽  
Meimei Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electric Vehicle ◽  
Load Forecasting ◽  
Short Term ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Lion Algorithm ◽  
Short Term Load Forecasting ◽  
Vehicle Charging

scholarly journals Load forecasting of electric vehicle charging station based on grey theory and neural network

2021 ◽  
Vol 7 ◽  
pp. 487-492
Author(s):  
Jiawei Feng ◽  
Junyou Yang ◽  
Yunlu Li ◽  
Haixin Wang ◽  
Huichao Ji ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Load Forecasting ◽  
Grey Theory ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

scholarly journals Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88058-88071 ◽  
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Yanlu Xu ◽  
Tengteng Xu ◽  
Chenxu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Multi Scale ◽  
Short Term Load Forecasting

scholarly journals Research on electric vehicle load forecasting based on travel data

2021 ◽  
Vol 257 ◽  
pp. 01017
Author(s):  
Lin Xu ◽  
Bing Wang ◽  
Mingxi Cheng ◽  
Shangshang Fang
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Simulation Analysis ◽  
Load Forecasting ◽  
Charging Station ◽  
Demand Distribution ◽  
Electric Vehicle Charging ◽  
Other Information ◽  
Vehicle Charging

Due to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric vehicle charging station based on travel data. Firstly, the didi trip data is processed and mined to get the trip matrix and other information. Then, the electric vehicle charging load forecasting model is established based on the established unit mileage power consumption model and charging model, and the charging demand distribution information is predicted by Monte Carlo method. Finally, the simulation analysis is carried out based on the trip data of some areas of a city, which shows the effectiveness of the established model feasibility.

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