基于二次模态分解的LSTM短期电力负荷预测

为进一步提高短期电力负荷的预测精度，需要更深层次发掘负荷数据中隐藏的非线性关系。提出一种基于信号分解技术的二次模态分解的长短期记忆神经网络(long short-term memory network, LSTM)用于电力负荷的短期预测。所提算法先对原始负荷序列进行自适应噪声的完全集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN),再将CEEMDAN分解后分量中的强非平稳分量进行变分模态分解(variational mode decomposition, VMD),同时用中心频率法对VMD分解个数进行优化，然后将两次分解后得到的负荷子序列送入LSTM中进行预测，并将所得分量预测结果进行叠加。结果表明，本文所提方法对短期电力负荷预测结果精度和模型性能都有较大提升。

LSTM short-term power load forecasting based on quadratic mode decomposition

To further improve the accuracy of short-term power load prediction, it is necessary to explore the hidden nonlinear relationships in load data at a deeper level. In this paper, a short-term memory neural network based on quadratic modal decomposition of signal decomposition technology is proposed for short-term power load forecasting. Firstly, The proposed algorithm decomposed the original load data using the CEEMDAN algorithm. Secondly, the strong non-stationary components in the CEEMDAN decomposed components were decomposed by the VMD algorithm. at the same time, the number of VMD decomposition was optimized by using the central frequency method, and then the load subsequences obtained after two decompositions were fed into LSTM for prediction, and the predicted results of the obtained components were overlaid. The results indicate that the method proposed in this article has significantly improved the accuracy of short-term power load forecasting results and model performance.