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基于注意力机制编码器-解码器结构的充电负荷预测方法

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为提升细时间颗粒度充电负荷预测的准确率,提出了一种基于注意力机制编码器-解码器结构的新方法.首先,在编码器中使用多头自注意力机制对站点历史充电负荷序列的时间依赖进行编码;然后,采用非自回归时空解码器,分别运用多头自注意力机制和多头互注意力机制,计算站点空间依赖和历史充电负荷的相关性;最后,在实际充电负荷数据上进行对比试验,验证了所提方法具有更高的准确度.结果表明,基于注意力机制的编码器-解码器方法能够有效捕获负荷序列时间依赖与充电站的空间依赖.
Charging Load Prediction Method Based on the Encoder-decoder Structure of Attention Mechanis
A novel method based on the encoder-decoder structure of attention mechanism was proposed to improve the accuracy of fine-time-granularity charging load prediction.Firstly,a multi head self-attention mechanism was used to encode the time dependence of the station's historical charging load sequence;then,a non-autoregressive spatiotemporal decoder was used to calculate the correlation between site spatial dependence and historical charging load using multi head self-attention mechanism and multi head mutual attention mechanism,respectively;finally,comparative experiments were conducted on actual charging load data to verify whether the proposed method has higher accuracy.The results indicate that the encoder-decoder method based on attention mechanism can effectively capture the time dependence of load sequence and the spatial dependence of charging station.

attention mechanismencoder-decodercharging stationcharging load forecastingtemporal dependencespatial dependence

鞠晨、易媛、施元杰

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上海联联睿科能源科技有限公司,上海 200063

上海电器科学研究所(集团)有限公司,上海 200063

上海电力实业有限公司,上海 200001

注意力机制 编码器-解码器 充电站 充电负荷预测 时间依赖 空间依赖

2024

电气自动化
上海电气自动化设计研究所有限公司 上海市自动化学会

电气自动化

CSTPCD
影响因子:0.377
ISSN:1000-3886
年,卷(期):2024.46(6)