Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation
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 gr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/18/4893 |
| _version_ | 1797553281662189568 |
|---|---|
| author | Byungsung Lee Haesung Lee Hyun Ahn |
| author_facet | Byungsung Lee Haesung Lee Hyun Ahn |
| author_sort | Byungsung Lee |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T16:14:10Z |
| format | Article |
| id | doaj.art-01b661d9975741e3a45bfe60630041af |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T16:14:10Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-01b661d9975741e3a45bfe60630041af2023-11-20T14:11:41ZengMDPI AGEnergies1996-10732020-09-011318489310.3390/en13184893Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data ImputationByungsung Lee0Haesung Lee1Hyun Ahn2Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, KoreaSmart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, KoreaDivision of Computer Science and Engineering, Kyonggi University, Gyeonggi 16227, KoreaAs 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%.https://www.mdpi.com/1996-1073/13/18/4893electric vehiclesload forecastinglong short-term memorymissing valuesdata imputation |
| spellingShingle | Byungsung Lee Haesung Lee Hyun Ahn Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation Energies electric vehicles load forecasting long short-term memory missing values data imputation |
| title | Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation |
| title_full | Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation |
| title_fullStr | Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation |
| title_full_unstemmed | Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation |
| title_short | Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation |
| title_sort | improving load forecasting of electric vehicle charging stations through missing data imputation |
| topic | electric vehicles load forecasting long short-term memory missing values data imputation |
| url | https://www.mdpi.com/1996-1073/13/18/4893 |
| work_keys_str_mv | AT byungsunglee improvingloadforecastingofelectricvehiclechargingstationsthroughmissingdataimputation AT haesunglee improvingloadforecastingofelectricvehiclechargingstationsthroughmissingdataimputation AT hyunahn improvingloadforecastingofelectricvehiclechargingstationsthroughmissingdataimputation |