摘要
针对碳交易过程中碳价序列的非线性和非平稳性,提出一种基于多模式分解、样本熵、鲸鱼优化(whale optimization algorithm,WOA)和长短期记忆神经网络(long short-term memory,LSTM)的组合预测模型。首先,使用奇异谱分解、变分模态分解和完全集合经验模态分解,分别分解原始碳价序列,降低原始序列的复杂度和非平稳性,实现不同模式模态分量规律的互补;然后,使用样本熵算法将熵值接近分量重构为一个新的分量,以提高预测效率;最后,使用WOA-LSTM组合预测网络建立历史碳价之间的时间特征关系,在时空相关性分析的基础上得到碳价预测值。实验结果表明,该组合预测模型可以有效地提高碳交易价格的预测准确率。
Taking into account the non-linearity and non-stationarity of the carbon price series in carbon trading, this paper proposes a combined prediction model which is based on the multi-mode decomposition, sample entropy, the whale optimization algorithm and the LSTM neural network for predicting carbon trading price.Firstly, the original carbon price series are decomposed by using the singular spectrum decomposition(SSD), the variational modal decomposition(VMD) and the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) respectively to reduce the complexity and non-stationarity of the original data and realize the complementation of the modal components regular pattern of different modes.Secondly, the sample entropy algorithm is used to reconstruct the entropy close component into a new component to improve the prediction efficiency.Finally, the WOA-LSTM combined prediction network is used to establish the time characteristic relationship between historical carbon trading prices, and the final prediction results are obtained based on the spatio-temporal correlation analysis.The experiment results show that the combined prediction model based on multi-mode decomposition-sample entropy-WOA-LSTM can improve the accuracy of carbon trading price prediction effectively.
作者
赵鑫
陈臣鹏
毕贵红
陈仕龙
谢旭
ZHAO Xin;CHEN Chenpeng;BI Guihong;CHEN Shilong;XIE Xu(School of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电力科学与工程》
2022年第2期52-59,共8页
Electric Power Science and Engineering
关键词
碳交易价格
多模式分解
样本熵
鲸鱼优化
LSTM神经网络
组合预测
carbon trading price
multi-mode decomposition
sample entropy
whale optimization algorithm
LSTM neural network
combined prediction
