为提高光伏功率的预测精度,提出一种基于完全自适应噪声经验模态分解（CEEMDAN）和串行卷积神经网络及门控神经网络（CNN-GRU）的光伏功率组合预测模型。首先,针对光伏功率波动性对预测结果的影响,采用CEEMDAN将原始光伏功率分解为若干子序列降低序列的非平稳性,并通过样本熵（SE）计算各子序列的复杂度,将SE值相近的序列,进行重组以减少计算量。其次,为克服单一神经网络在学习光伏功率历史数据特征的局限性,提出串行CNN-GRU混合神经网络以充分挖掘光伏功率的时空特征;将各子序列输入串行CNN-GRU得到预测结果,并将子序列预测结果叠加得到光伏功率预测结果。最后,对两个地区的光伏电站进行实例验证,同时构建LSTM、GRU、CEEMDAN-LSTM、CEEMDAN-GRU和串行CNN-GRU,进行对比验证。结果表明,所提模型能得到良好的预测结果,拥有良好的预测精度和泛化能力。
Abstract
To enhance the accuracy of PV power forecasting, this paper proposes a combined forecasting model for PV power based on CEEMDAN and a serial CNN-GRU network. Firstly, considering the impact of PV power fluctuations on the forecasting results, CEEMDAN is used to decompose the original PV power into several subsequences to reduce the non-stationarity of the sequence. Each subsequence’s sample entropy（SE） is then calculated to measure its complexity, and subsequences with similar SE values are regrouped to reduce computational load. Secondly, to overcome the limitations of a single neural network in learning the historical characteristics of PV power, a serial CNN-GRU hybrid neural network is proposed to explore the spatiotemporal features of PV powerfully. Each subsequence is input into the serial CNN-GRU network to obtain the forecasting results, and the predicted values of the subsequences are summed to yield the final PV power forecasting results. Finally, case studies on PV power plants in two regions are conducted, with comparative validation against LSTM, GRU, CEEMDAN-LSTM, CEEMDAN-GRU, and the serial CNN-GRU models. The results show that the proposed model achieves superior forecasting accuracy and generalization capability.
关键词
光伏功率预测 /
CNN /
GRU /
混合神经网络 /
CEEMDAN /
SE
Key words
photovoltaic power forecasting /
CNN /
GRU /
hybrid neural networks /
CEEMDAN /
SE
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