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Abstract：
Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.
摘要：
针对电力负荷的高随机性和波动性以及难以准确预测的问题,本文提出了一种基于CEEMDAN（完全集成经验模态分解）和TCN-LSTM（时间卷积网络和长短期记忆网络）的电力负荷预测方法。该方法结合CEEMDAN对原始负荷数据的分解和TCN-LSTM模型的时空建模能力,提高预测的准确性和稳定性。首先,CEEMDAN将原始负荷数据分解为多个线性稳定子序列，然后引入样本熵对每个子序列进行重组。然后将重组后的序列用作TCN-LSTM模型的输入，以提取序列特征并进行训练和预测。通过选择新南威尔士州的电力合规性数据进行建模预测，澳大利亚,并与传统预测方法进行了比较。实验结果表明,本文提出的算法对负荷预测具有较高的精度和较好的预测效果,可为电力负荷预测方法提供部分参考。
