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Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning Technique

机译:基于Q学习技术的电动汽车充电站的加固载荷预测

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摘要

The electric vehicles' (EVs) rapid growth can potentially lead power grids to face new challenges due to load profile changes. To this end, a new method is presented to forecast the EV charging station loads with machine learning techniques. The plug-in hybrid EVs (PHEVs) charging can be categorized into three main techniques (smart, uncoordinated, and coordinated). To have a good prediction of the future PHEV loads in this article, the Q-learning technique, which is a kind of the reinforcement learning, is used for different charging scenarios. The proposed Q-learning technique improves the forecasting of the conventional artificial intelligence techniques such as the recurrent neural network and the artificial neural network. Results prove that PHEV loads can accurately be forecasted by using the Q-learning technique under three different scenarios (smart, uncoordinated, and coordinated). The simulations of three different scenarios are obtained in the Keras open source software to validate the effectiveness and advantages of the proposed Q-learning technique.
机译:电动车辆(EVS)快速增长可以潜在地引领电网,以面临由于负载轮廓变化而产生的新挑战。为此,提出了一种新方法以预测具有机器学习技术的EV充电站负载。插件混合动力EVS(PHEVS)充电可以分为三种主要技术(智能,不协调和协调)。为了良好地预测本文中未来的PHEV载荷,Q学习技术是一种加强学习,用于不同的充电情景。所提出的Q学习技术改善了传统人工智能技术的预测,例如经常性神经网络和人工神经网络。结果证明,通过使用三种不同场景(智能,未开销和协调)的Q学习技术,可以准确地预测PHEV加载。在Keras开源软件中获得了三种不同场景的模拟,以验证所提出的Q学习技术的有效性和优点。

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