针对自注意力机制难以捕获光伏发电功率序列中潜在的周期信息的问题,提出一种基于周期注意力机制的光伏发电功率预测模型Periodformer。首先利用周期切片与嵌入模块对原始序列进行频域变换得到其潜在周期,并按照潜在周期对序列切片、堆叠、得到一系列3D序列块;其次在编码阶段提出周期增强模块捕获序列子周期间和周期内的特征;最后在解码阶段提出周期交叉注意力模块对每个3D序列块分别进行预测,将各3D序列块的预测结果卷积融合得到预测结果。实验分析证明,所提模型能够很好地捕捉到周期内和周期间的潜在特征,具有较高的预测性能;在中长期光伏发电功率预测任务上相较基准模型仍能保持较好的效果。
Abstract
In order to solve the problem that the self-attention mechanism is difficult to capture the potential periodic information in the photovoltaic power sequence, a photovoltaic power prediction model Periodformer based on periodic attention mechanism is proposed. Firstly, the potential period of the original sequence is obtained by frequency domain transformation of the period slice and embedding module, and a series of 3D sequence blocks are obtained by slicing and stacking according to the potential period. Secondly, in the oncoding stage, a period strength block is proposed to capture the features during and within the sub-cycle of the sequence. Finally, in the decoding stage, a period cross-attention module is proposed to predict each 3D sequence block respectively, and the prediction results are convoluted and fused to get the prediction results. Experimental analysis shows that the proposed model can well capture the potential characteristics during and within cycles, and has higher prediction performance, and can still maintain a better effect compared with the benchmark model in the medium-and long-term photovoltaic power prediction task.
关键词
光伏发电 /
可再生能源 /
功率预测 /
神经网络 /
注意力机制
Key words
PV power /
renewable energy /
power forecasting /
neural networks /
attention mechanism
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