居民充电桩的容量预测可为其定容选址提供参考，助力实现“双碳”目标，为此提出了一种基于数据驱动的居民充电桩容量预测方法。首先，采集了居民充电桩的历史容量数据并进行预处理；其次，利用不同大小的时序窗口对其进行切片作为输入特征；最后，构建了结合多头注意力机制和门控循环单元神经网络的预测模型，将特征输入模型从而实现了对未来容量的精准预测。通过实例分析表明，该模型预测结果的平均绝对误差和均方根误差分别为33.19和102.14%，预测精度相较于其他模型有较大提升，为居民充电桩的容量预测提供了新思路。

The capacity prediction of residential charging stations can provide a reference for its capacity selection and contribute to the carbon peaking and caron neutrality goals. In this regard, a datadriven method for predicting the capacity of residential charging stations is proposed. Firstly, historical capacity data of residential charging stations are collected and preprocessed. Secondly, differentsized time windows are used to slice the data as input features. Finally, a prediction model combining multihead attention mechanism and gated recurrent unit neural network is constructed, and the features are input into the model to achieve accurate prediction of future capacity. The results of the case analysis show that the exponential mean absolute error and exponential root mean square error of the model are 33.19 and 102.14% respectively. Compared to other models, the proposed model significantly improves the prediction accuracy and provides new insights for capacity prediction of residential charging stations.

国家自然科学联合基金（U1934204）；四川省区域创新合作项目（21QYCXOO96）