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| Research of Short-Term Load Forecasting Model for Electrical Vehicle Charging Stations based on PSO-SNN |
| Wang Zhe1, Dai Bingqi1, Li Xiangdong2 |
1. Qingdao University, Qingdao, Shandong 266071;
2. Maintenance Company of Shandong Power Company, Ji’nan 250000 |
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Abstract The load characteristic of the electric vehicle charging station is analyzed based on the weather forecast data and measured power data. A short-term load forecasting model for electrical vehicle charging stations based on particle swarm optimized spike neural network is built in this paper. Spike neural network encode information in the timing of single spike, making it with strong calculating ability, large information capacity and good real time capability. Verifies with simulation example, the errors of prediction model proposed in this paper are less than the traditional BP-NN model for 8.59%、9.28%、12.06% and 8.72% respectively in four seasons, so the model has a better prediction accuracy.
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Published: 13 January 2016
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| Cite this article: |
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Wang Zhe,Dai Bingqi,Li Xiangdong. Research of Short-Term Load Forecasting Model for Electrical Vehicle Charging Stations based on PSO-SNN[J]. Electrical Engineering, 2016, 17(1): 46-50.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2016/V17/I1/46
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2016, Vol. 17 
