Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Byungsung Lee, Haesung Lee and Hyun Ahn
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Byungsung Lee: Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, Korea
Haesung Lee: Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, Korea
Hyun Ahn: Division of Computer Science and Engineering, Kyonggi University, Gyeonggi 16227, Korea

Energies, 2020, vol. 13, issue 18, 1-15

Abstract: As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Keywords: electric vehicles; load forecasting; long short-term memory; missing values; data imputation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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