期刊
ENERGIES
卷 12, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/en12142692
关键词
load forecasting; LSTM; electric vehicles; deep learning
资金
- China NSFC [51607177]
- Natural Science Foundation of Guangdong Province [2018A030310671, 2018A030313755]
- China Post-Doctoral Science Foundation [2018M631005]
- European Commission [774431]
- Outstanding Young Researcher Innovation Fund of the Shenzhen Institute of Advanced Technology
- Chinese Academy of Sciences [201822]
- Shenzhen Basic Research Fund [JCYJ20160331190123578]
- Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence
- H2020 Societal Challenges Programme [774431] Funding Source: H2020 Societal Challenges Programme
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.
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