Abstract Accurately predicting the spatial-temporal distribution of electric vehicles (EVs) load is of great significance to the optimal dispatching and safe operation of the power grid. This paper proposes a spatio-temporal distribution prediction method for EV charging loads, which considers the charging behavior characteristics of different types of EVs and the spatio-temporal coupling between EVs and charging stations. Firstly, an EV charging demand prediction model based on improved random forest (IRF) is established, in which the parameters of random forest (RF) prediction model for each type of EVs are optimized by harmony search (HS) with the principle of minimum prediction error to release the sensitivity of the EV charging prediction model to parameters. Then, a bottom-up method for predicting the spatial and temporal distribution of EV cluster charging loads based on IRF is proposed, which takes into account the spatial-temporal coupling between EVs and charging stations. In addition, a parallel computational method with data parallelization and task parallelization is proposed to enhance the efficiency and practicability of the proposed method. Finally, the accuracy level of IRF has been explored through rigorous case studies comparing with support vector machine (SVM), back propagation neural network (BPNN) and general random forest (RF). The case studies also reveal that the proposed method, compared with traditional methods, can not only improve the prediction accuracy of the total EV charging load but also obtain the spatial and temporal distribution of the charging load in the region.

中文翻译：

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考虑充电行为的电动汽车负载曲线的数据驱动时空预测

摘要 准确预测电动汽车（EV）负荷的时空分布对电网优化调度和安全运行具有重要意义。本文提出了一种电动汽车充电负荷时空分布预测方法，该方法考虑了不同类型电动汽车的充电行为特点以及电动汽车与充电站之间的时空耦合。首先，建立了基于改进随机森林（IRF）的电动汽车充电需求预测模型，通过和声搜索（HS），以最小预测为原则，对各类电动汽车的随机森林（RF）预测模型参数进行优化。错误释放电动汽车充电预测模型对参数的敏感性。然后，提出了一种基于IRF的电动汽车集群充电负荷时空分布预测方法，该方法考虑了电动汽车与充电站之间的时空耦合。此外，提出了一种具有数据并行化和任务并行化的并行计算方法，以提高所提出方法的效率和实用性。最后，通过与支持向量机 (SVM)、反向传播神经网络 (BPNN) 和一般随机森林 (RF) 进行比较的严格案例研究，探索了 IRF 的准确度水平。案例研究还表明，与传统方法相比，所提出的方法不仅可以提高电动汽车总充电负荷的预测精度，而且可以获得该地区充电负荷的时空分布。