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A novel LSTM based deep learning approach for

multi-time scale electric vehicles charging load

prediction

Juncheng Zhu, Zhile Yang, Yan Chang, Yuanjun Guo, Kevin Zhu, Jianhua Zhang

Abstract—Short-term load forecasting is an important issue

in energy management system and a key measure to maintain

the stable and effective operation of power systems, providing

reasonable future load curve feeding to the unit commitment and

economic load dispatch. Speciﬁcally, load forecasting at different

time scales has various and corresponding roles in power systems.

In recent years, plug-in electric vehicles have gradually become

popular in major cities, and the number of electric vehicles is

growing rapidly. However, the mass roll out of EVs may cause

severe problems to the power system due to the huge charging

power and stochastic charging behaviours of the EVs drivers. The

accurate model of EVs charging load forecasting is therefore an

emerging topic. In this paper, artiﬁcial neural networks and a

long short term memory model based deep learning approaches

are employed and compared in forecasting the EVs charging

load from the charging station perspective. Numerical results

show that the long short term memory model has demonstrated

better performance and provided a model of higher accuracy in

short-term EVs load forecasting comparing with the traditional

artiﬁcial neural networks.

I. INTRODUCTION

Power load forecasting has long been an important com-

ponent in power and energy sectors and well studied since

the 1980s [1]. Featured load forecasting is based on the

operating characteristics of the system, capacity-enhancing

decisions, natural conditions as well as the social impacts,

determining load demand at particular time slots in the future

under conditions and providing fundamental reference for

power system scheduling. Accurate load forecasting could

signiﬁcantly contribute to various aspects including econom-

ical and reasonable arrangement of the start and stop of

internal generator sets of a power grid, maintaining the safety

and stability of the grid operation, reducing the unnecessary

This research is ﬁnancially supported by China NSFC under grants

61773252, Natural Science Foundation of Guangdong Province under grants

2018A030310671, China Post-doctoral Science Foundation (2018M631005),

and State Key Laboratory of Alternate Electrical Power System with Renew-

able Energy Sources (Grant No. LAPS18020)

Juncheng Zhu is the Zhengzhou University, Zhengzhou, 450001, China (e-

mail: jczhu

siat@163.com)

Zhile Yang and Yuanjun Guo are with Shenzhen Institute of Advanced

Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055,

China (e-mail:zl.yang,yj.guo@siat.ac.cn)

Yan Chang is with University of Science and Technology of China, Hefei

230026, China (e-mail: sa517011@mail.ustc.edu.cn)

Kevin Zhu is with Lynbrook High School, San Jose, CA, USA,

(email:Kevinrzhu123@gmail.com)

Jianhua Zhang is with the State Key Laboratory of Alternate Electrical

Power System with Renewable Energy Sources, North China Electric Power

University, Beijing 102206, China (e-mail: zjh@ncepu.edu.cn)

rotating reserve capacity, and eventually improving economic

and social beneﬁts. Load forecasting could be divided into

ultra-short-term load forecasting, short-term load forecasting,

medium-term load forecasting and long-term load forecasting

depending on the expected time length. The ultra-short-term

load forecasting refers to the load forecast within 1 hour ahead.

In the safety monitoring state, the predicted values of 5 to 10

second or 1 to 5 minute are required, and the preventive control

and emergency state processing require a predicted value of

10 minute to 1 hour. Various research approaches have been

developed in predicting the power demand in multiple time

scales.

The state-of-the-art power load forecasting models can

be categorized into traditional statistical models and artiﬁ-

cial intelligence (AI) models. Traditional forecasting methods

majorly include the time series method [2], autoregressive

integrated moving average [3], regression analysis [4], Kalman

ﬁltering [5], etc. On the other hand, AI methods consist of

artiﬁcial neural networks (ANN) [6], support vector machines

(SVM) [7], and deep learning methods [8]. Before the 21st

century, due to the strong adaptive, self-learning and genere-

lization ability, ANN had become an important technique in

load forecasting. Hippert et al. [9] reviewed the application

of ANNs for load forecasting and claimed that ANNs have

effectiveness for load forecasting in terms of the accuracy and

efﬁciency.

In recent years, the deep learning methods have been

obtained wide attractions and used in image semantic seg-

mentation [10], classiﬁcation [11], target detection [12], nat-

ural language processing [13] and many other science and

engineering ﬁelds. The network structure constructed by the

deep learning methods are more complex with a large number

of hidden layers and/or recurrent structure, which endowing

stronger learning and self-adaptive ability than ANN methods.

Therefore, it has also been paid attention in the ﬁeld of load

forecasting. In 1998, Vermaak and Botha [14] used recurrent

neural network (RNN) for the ﬁrst time to establish a short-

term load forecasting model. However, the conventional RNN

would suffer from the gradient vanishing problem and the long

short term memory (LSTM) network is an effective approach

to relief the issue. More recently, Marino et al. [15] proposed a

LSTM architecture to forecast the load of individual residences

[16]. Kong et al. [17] combined the energy consumption of

a residence with the behaviour of a resident, converted the

behaviour patterns of energy consumers into a sequence of

3531

2019 IEEE PES Innovative Smart Grid Technologies Asia

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