基于聚类算法和转换网络的光伏短期功率预测

精准的光伏功率预测对优化光伏电站的运行和管理以及提高光伏发电的效率具有重要的作用。本文提出了一种基于聚类算法和转换网络的光伏短期功率预测方法。该方法首先基于自编码器的无监督聚类算法对光伏短期功率数据进行了预处理，以降低光伏出力数据本身的不稳定性对功率预测的影响。之后，该方法使用具有自注意力机制和多头注意力机制的转换网络进行光伏短期功率的预测。转换网络由编码器和解码器组成。转换网络相比传统的循环神经网络(RNN)更善于挖掘时序之间的关系。注意力机制使得转换网络具有并行计算的能力，可以加快网络训练的速度。最后，在澳大利亚光伏功率与气象数据中心 (DKASC)的光伏数据集上验证了本文提出的光伏短期功率预测方法。实验结果表明，本文提出的方法具有令人满意的预测精度。

Short-term photovoltaic power prediction based on clustering algorithm and transformer network

Accurate photovoltaic power prediction plays an important role in optimizing the operation and management of photovoltaic power stations and improving the efficiency of photovoltaic power generation. In this paper, a short-term photovoltaic power prediction method based on clustering algorithm and conversion network is proposed. In this method, short-term photovoltaic power data is preprocessed based on unsupervised clustering algorithm of autoencoder to reduce the influence of instability of photovoltaic output data on power prediction. Then, the method uses a conversion network with self-attention mechanism and multi-attention mechanism to predict short-term photovoltaic power. The conversion network consists of encoder and decoder. Compared with traditional recurrent neural networks (RNN), transformational networks are better at mining the relationship between time sequences. The attention mechanism makes the switching network have the capability of parallel computation, which can accelerate the speed of network training. Finally, the proposed method is verified on the photovoltaic data set of the Australian Photovoltaic Power and Meteorological Data Center (DKASC). Experimental results show that the proposed method has satisfactory prediction accuracy.