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

Zhu, Juncheng; Yang, Zhile; Mourshed, Monjur; Guo, Yuanjun; Zhou, Yimin; Chang, Yong; Wei, Yanjie; Shi, Feng

doi:10.3390/en12142692

Cited by 157 publications

(81 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GRU model using 1 hidden layer provided the best performance with normalized RMSE (NRMSE) of 2.89%. Other studies [87] have looked at even higher resolution of time series data, and provided EV charging load forecasting for super short term (minutely data). A comparative study of various DL models showed that LSTM performs better than conventional ANNs by reducing the forecasting error by more than 30%.…”

Section: Deep Learning For Analysis and Prediction Of Charging Behmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

View full text Add to dashboard Cite

As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

show abstract

Section: Deep Learning For Analysis and Prediction Of Charging Behmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The deep learning algorithm has the characteristics of information memory, self-learning, optimization calculation, etc. It also has strong computing power, complex mapping ability, and various intelligent processing capabilities [25].…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

Research on Short-Term Load Prediction Based on Seq2seq Model

Gong

Mahato

et al. 2019

Energies

View full text Add to dashboard Cite

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

show abstract

“…A comparison of different approaches was also possible. For super-short-term forecasting with deep learning, the long-short-term memory (LSTM) already showed very realistic results as described in [5].…”

Section: Introductionmentioning

confidence: 97%

Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)

et al. 2020

View full text Add to dashboard Cite

From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.

show abstract

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

Cited by 157 publications

References 46 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Research on Short-Term Load Prediction Based on Seq2seq Model

Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)

Contact Info

Product

Resources

About