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| Research of Short-Term Load Forecasting for Electrical Vehicle Charging Station based on Combined Prediction Model |
| Chen Xiqiang1, Liu Zhixin2, Li Xingbo3 |
1. Taian Power Supply Company of Shandong Power Company, Taian, Shangdong 271000;
2. Gaomi Power Supply Company of Shandong Power Company, Gaomi, Shangdong 261500;
3. Pingyin Power Supply Company of Shandong Power Company, Pingyin, Shangdong 250400 |
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Abstract The electrical vehicle charging station has a problem of big randomness. In order to solve this problem, this article sets up the ELMAN neural network prediction model. Then this article optimizes the model by Particle Swarm optimization. Then this article combines the optimized model with fuzzy control, and sets up a combined prediction model based on three models. This article collects the real load data of a electrical vehicle charging station in Qingdao. Lastly, results show that the above combined prediction model is effective, and this combined prediction model can improve the predict accuracy.
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Published: 24 February 2017
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| Cite this article: |
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Chen Xiqiang,Liu Zhixin,Li Xingbo. Research of Short-Term Load Forecasting for Electrical Vehicle Charging Station based on Combined Prediction Model[J]. Electrical Engineering, 2017, 18(2): 59-64.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2017/V18/I2/59
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2017, Vol. 18 
