• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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An LSTM Neural Network Model for Forecasting Daily Peak Electric Load of EV Charging Stations


Haesung Lee, Byungsung Lee, Hyun Ahn, Journal of Internet Computing and Services, Vol. 21, No. 5, pp. 119-127, Oct. 2020
10.7472/jksii.2020.21.5.119, Full Text:
Keywords: EV, Peak electric load, Load forecasting, Deep Learning, LSTM

Abstract

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.


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Cite this article
[APA Style]
Lee, H., Lee, B., & Ahn, H. (2020). An LSTM Neural Network Model for Forecasting Daily Peak Electric Load of EV Charging Stations. Journal of Internet Computing and Services, 21(5), 119-127. DOI: 10.7472/jksii.2020.21.5.119.

[IEEE Style]
H. Lee, B. Lee, H. Ahn, "An LSTM Neural Network Model for Forecasting Daily Peak Electric Load of EV Charging Stations," Journal of Internet Computing and Services, vol. 21, no. 5, pp. 119-127, 2020. DOI: 10.7472/jksii.2020.21.5.119.

[ACM Style]
Haesung Lee, Byungsung Lee, and Hyun Ahn. 2020. An LSTM Neural Network Model for Forecasting Daily Peak Electric Load of EV Charging Stations. Journal of Internet Computing and Services, 21, 5, (2020), 119-127. DOI: 10.7472/jksii.2020.21.5.119.