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首页> 外文会议>2013 48th International Universities' Power Engineering Conference >Forecasting Electric Vehicle charging demand using Support Vector Machines
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Forecasting Electric Vehicle charging demand using Support Vector Machines

机译：使用支持向量机预测电动汽车的充电需求

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摘要

Road transport today is dominated by oil-delivered fuels and internal combustion engines and such a high level of dependence on one single source of primary energy carries strategic, climatic and economic risks [1]. Electric mobility offers an opportunity for diversification of the primary energy sources used in transport, but also brings new risks, technological challenges and commercial imperatives. Large penetration of Electric Vehicles (EV) will increase the electricity demand and load forecasting plays a central role in the operation and planning of electric power. This paper proposes a short-term load forecast model using Support Vector Machines, an artificial intelligence technique. A realistic scenario is studied to test the performance of the suggested model. The accuracy of the method is evaluated through a comparison with a Monte Carlo forecasting technique.
机译：当今的公路运输以输油燃料和内燃机为主导，对单一单一主要能源的高度依赖具有战略，气候和经济风险[1]。电动交通为运输中使用的主要能源的多样化提供了机会，但也带来了新的风险，技术挑战和商业需求。电动汽车（EV）的广泛普及将增加电力需求，而负荷预测在电力的运营和规划中起着核心作用。本文提出了一种使用人工智能技术支持向量机的短期负荷预测模型。研究了一个实际场景来测试建议模型的性能。通过与蒙特卡洛预测技术进行比较来评估该方法的准确性。

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