Research on electric vehicle load forecasting considering regional special event characteristics
With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1341246/full |
| _version_ | 1797316019210944512 |
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| author | Tuo Xie Yu Zhang Gang Zhang Kaoshe Zhang Hua Li Xin He |
| author_facet | Tuo Xie Yu Zhang Gang Zhang Kaoshe Zhang Hua Li Xin He |
| author_sort | Tuo Xie |
| collection | DOAJ |
| description | With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic. |
| first_indexed | 2024-03-08T03:12:16Z |
| format | Article |
| id | doaj.art-eaa22fe57e464129b0a8bf936781dd6d |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-03-08T03:12:16Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-eaa22fe57e464129b0a8bf936781dd6d2024-02-13T04:35:51ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-02-011210.3389/fenrg.2024.13412461341246Research on electric vehicle load forecasting considering regional special event characteristicsTuo Xie0Yu Zhang1Gang Zhang2Kaoshe Zhang3Hua Li4Xin He5School of Electrical Engineering, Xi’an University of Technology, Xi’an, ChinaState Grid Shaanxi Electric Power Co, LTD. Ultra High Voltage Company, Xi’an, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an, ChinaElectric Power Research Institute of State Grid Shaanxi Electric Power Company, Xi’an, ChinaSchool of Water Resources and Hydropower, Xi’an University of Technology, Xi’an, ChinaWith the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1341246/fullelectric vehicle charging load forecastfeature correlationmaximum relevant minimum redundancyimproved particle swarm optimization -long short term memoryspecial event characteristics |
| spellingShingle | Tuo Xie Yu Zhang Gang Zhang Kaoshe Zhang Hua Li Xin He Research on electric vehicle load forecasting considering regional special event characteristics Frontiers in Energy Research electric vehicle charging load forecast feature correlation maximum relevant minimum redundancy improved particle swarm optimization -long short term memory special event characteristics |
| title | Research on electric vehicle load forecasting considering regional special event characteristics |
| title_full | Research on electric vehicle load forecasting considering regional special event characteristics |
| title_fullStr | Research on electric vehicle load forecasting considering regional special event characteristics |
| title_full_unstemmed | Research on electric vehicle load forecasting considering regional special event characteristics |
| title_short | Research on electric vehicle load forecasting considering regional special event characteristics |
| title_sort | research on electric vehicle load forecasting considering regional special event characteristics |
| topic | electric vehicle charging load forecast feature correlation maximum relevant minimum redundancy improved particle swarm optimization -long short term memory special event characteristics |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1341246/full |
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