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Ultra-Short-Term Photovoltaic Power Prediction Based on Self-Attention Mechanism and Multi-Task Learning

机译：基于自我关注机制和多任务学习的超短术光伏电源预测

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摘要

Due to the volatility and randomness of the photovoltaic power generation, it is difficult for traditional models to predict it accurately. To solve the problem, we established a model based on the self-attention mechanism and multi-task learning to predict the ultra-short-term photovoltaic power generation. First, we selected the data with the optimal timing length and input the data into the Encoder-Decoder network based on the self-attention. The validity of features extracted by the encoder was checked by the decoder. Then, we added a restriction to the middle layer of the Encoder-Decoder network to prevent the autoencoder from copying the input to the output mechanically. This condition is used to predict the photovoltaic power generation, so a multi-task learning model was established. Finally, to take full advantage of the features that are efficiently expressed and allow our main task, the prediction task, to learn some unique features autonomously, we proposed a step-by-step training method and have validated the effectiveness of this view in experiments. Through experimental contrast, it is found that compared with the Encoder-Decoder network based on CNN and LSTM, the performance of the proposed method has been increased by 14.82 & x0025; and 8.09 & x0025; respectively. The RMSE and MAE of the Encoder-Decoder model based on the self-attention mechanism using step-by-step training are 0.071 and 0.040 respectively.
机译：由于光伏发电的波动性和随机性，传统模型很难准确地预测它。为了解决问题，我们建立了一种基于自我关注机制和多任务学习的模型，以预测超短术光伏发电。首先，我们选择了具有最佳定时长度的数据，并基于自我关注将数据输入到编码器解码器网络中。通过解码器检查编码器提取的功能的有效性。然后，我们为编码器 - 解码器网络的中间层添加了限制，以防止自动码器将输入机械复制到输出。这种情况用于预测光伏发电，因此建立了多任务学习模型。最后，为了充分利用有效表达并允许我们的主要任务，预测任务的特征，以自主地学习一些独特的特征，我们提出了一种逐步的训练方法，并验证了该视图在实验中的有效性。通过实验对比，发现与基于CNN和LSTM的编码器 - 解码器网络相比，所提出的方法的性能已增加14.82＆x0025;和8.09＆x0025;分别。基于使用逐步训练的自我关注机制的编码器 - 解码器模型的RMSE和MAE分别为0.071和0.040。

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