Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches
Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a pow...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/14/2692 |
| _version_ | 1811186152384757760 |
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| author | Juncheng Zhu Zhile Yang Monjur Mourshed Yuanjun Guo Yimin Zhou Yan Chang Yanjie Wei Shengzhong Feng |
| author_facet | Juncheng Zhu Zhile Yang Monjur Mourshed Yuanjun Guo Yimin Zhou Yan Chang Yanjie Wei Shengzhong Feng |
| author_sort | Juncheng Zhu |
| collection | DOAJ |
| description | Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model. |
| first_indexed | 2024-04-11T13:40:46Z |
| format | Article |
| id | doaj.art-820cbc64c2584ae6aa9bb4582c3e9ae7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:40:46Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-820cbc64c2584ae6aa9bb4582c3e9ae72022-12-22T04:21:14ZengMDPI AGEnergies1996-10732019-07-011214269210.3390/en12142692en12142692Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning ApproachesJuncheng Zhu0Zhile Yang1Monjur Mourshed2Yuanjun Guo3Yimin Zhou4Yan Chang5Yanjie Wei6Shengzhong Feng7School of Information Engineering, Zhengzhou University, Zhengzhou 450001, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSchool of Engineering, Cardiff University, Cardiff CF24 3AA, UKShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSchool of Software Engineering, University of Science and Technology of China, Hefei 230026, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaLoad forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.https://www.mdpi.com/1996-1073/12/14/2692load forecastingLSTMelectric vehiclesdeep learning |
| spellingShingle | Juncheng Zhu Zhile Yang Monjur Mourshed Yuanjun Guo Yimin Zhou Yan Chang Yanjie Wei Shengzhong Feng Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches Energies load forecasting LSTM electric vehicles deep learning |
| title | Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches |
| title_full | Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches |
| title_fullStr | Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches |
| title_full_unstemmed | Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches |
| title_short | Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches |
| title_sort | electric vehicle charging load forecasting a comparative study of deep learning approaches |
| topic | load forecasting LSTM electric vehicles deep learning |
| url | https://www.mdpi.com/1996-1073/12/14/2692 |
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