Wind power Prediction based on the fusion of CN N-GRU combined neural network and attention mechanism

Yankun Wang, Zhenda Song, Yuchen Liu, Jiacheng Chen
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Abstract

Accurate prediction of wind power plays an i mportant role in long-term stable and safe operation of pow er system. Wind power generation can save a lot of energy a nd solve the problem of high cost and low utilization rate of traditional thermal power generation. In this paper, a comb ination of CNN-GRU-Attention model is proposed, and thre e different data decomposition methods are used for data pr e-processing, and the optimal data pre-processing method is selected for the model. Meanwhile, the fusion model is comp ared with CNN-LSTM-ATTENTION, GRU, RNN and other traditional single models. By analyzing MAE, RSME, R an d other evaluation indexes, it is concluded that the predictio n accuracy of CNN-GRU-Attention model is the best. In wi nd power prediction, the predicted value of the model is clos est to the actual value, and the prediction effect is higher tha n LSTM, BPNN and other traditional networks, which is of great significance for wind turbine power prediction.
基于CN - N-GRU联合神经网络与注意力机制融合的风电预测
风电功率的准确预测对电力系统的长期稳定安全运行具有重要作用。风力发电可以节约大量能源,解决传统火力发电成本高、利用率低的问题。本文对CNN-GRU-Attention模型进行了梳理,采用三种不同的数据分解方法进行数据预处理,为模型选择最优的数据预处理方法。同时,将融合模型与CNN-LSTM-ATTENTION、GRU、RNN等传统单一模型进行了比较。通过对MAE、RSME、R等评价指标的分析,得出CNN-GRU-Attention模型的预测精度最好。在风电功率预测中,该模型预测值最接近实际值,预测效果高于LSTM、BPNN等传统网络,对风电功率预测具有重要意义。
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