Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques
Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist severa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/9/266 |
| _version_ | 1827722976104022016 |
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| author | Gayathry Vishnu Deepa Kaliyaperumal Peeta Basa Pati Alagar Karthick Nagesh Subbanna Aritra Ghosh |
| author_facet | Gayathry Vishnu Deepa Kaliyaperumal Peeta Basa Pati Alagar Karthick Nagesh Subbanna Aritra Ghosh |
| author_sort | Gayathry Vishnu |
| collection | DOAJ |
| description | Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW. |
| first_indexed | 2024-03-10T21:50:48Z |
| format | Article |
| id | doaj.art-a367531f7a084e11812ebc9c3dfff51b |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T21:50:48Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-a367531f7a084e11812ebc9c3dfff51b2023-11-19T13:27:33ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-09-0114926610.3390/wevj14090266Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning TechniquesGayathry Vishnu0Deepa Kaliyaperumal1Peeta Basa Pati2Alagar Karthick3Nagesh Subbanna4Aritra Ghosh5Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, IndiaDepartment of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, IndiaDepartment of Computer Science & Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, IndiaRenewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, IndiaAmrita Center for Wireless Networks and Applications, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Kollam 690525, Kerala, IndiaFaculty of Environment, Science and Economy (ESE), Renewable Energy, Electric and Electronic Engineering, University of Exeter, Penryn TR10 9FE, UKElectric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.https://www.mdpi.com/2032-6653/14/9/266electric vehiclesforecastingARFSVRLSTM |
| spellingShingle | Gayathry Vishnu Deepa Kaliyaperumal Peeta Basa Pati Alagar Karthick Nagesh Subbanna Aritra Ghosh Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques World Electric Vehicle Journal electric vehicles forecasting ARF SVR LSTM |
| title | Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques |
| title_full | Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques |
| title_fullStr | Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques |
| title_full_unstemmed | Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques |
| title_short | Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques |
| title_sort | short term forecasting of electric vehicle load using time series machine learning and deep learning techniques |
| topic | electric vehicles forecasting ARF SVR LSTM |
| url | https://www.mdpi.com/2032-6653/14/9/266 |
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