基于ICEEMDAN分解重构的BiLSTM-KELM短期电力负荷预测

BiLSTM-KELM Short-term Power Load Forecasting Based on ICEEMDAN Decomposition and Reconstruction

短期电力负荷预测在维持电力系统稳定运行、优化资源配置中发挥着至关重要的作用。针对电力负荷数据的复杂性和随机性以及现有预测模型的低精度问题,提出了一种新型的短期电力负荷预测模型。该模型包括改进的自适应噪声完备集经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)和排列熵(permutation entropy,PE)重构部分,以及双向长短期记忆神经网络(bidirectional long short-term memory,BiLSTM)与核极限学习(kernel extreme learning machine,KELM)预测部分。首先,使用ICEEMDAN将复杂的负荷数据分解成n个相对平稳的子序列,从而降低数据的随机性,并引入排列熵来计算每个子序列的PE值来进行重构,有效减小了模型的计算规模。其次,采用BiLSTM模型来挖掘数据之间的内在联系,对各个重构序列进行学习和预测。最后,利用KELM对重构序列的预测值进行非线性拟合,进一步提高预测精度。实验结果表明:ICEEMDAN-PE-BiLSTM-KELM模型比传统长短期记忆神经网络(long short-term memory,LSTM)模型的均方根误差(root mean square error,RMSE)降低了106.05 MW,平均绝对误差(mean absolute error,MAE)降低了62.34 MW,平均绝对百分比误差(mean absolute percentage error,MAPE)降低了0.877%,可见该模型能够更好地解决数据的复杂性和随机性,同时提高预测精度。

Short term power load forecasting plays a crucial role in maintaining stable operation of the power system and optimizing resource allocation.A new short-term power load forecasting model was proposed to address the complexity and randomness of power load data,as well as the low accuracy of existing forecasting models.This model included an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and permutation entropy(PE)reconstruction part,as well as a bidirectional long short-term memory(BiLSTM)and kernel extreme learning machine(KELM)prediction part.Firstly,ICEEMDAN was used to decompose complex load data into n relatively stable subsequences,thereby reducing the randomness of the data.Permutation entropy was introduced to calculate the PE value of each subsequence for reconstruction,effectively reducing the computational scale of the model.Secondly,the BiLSTM model was used to explore the intrinsic connections between data,and to learn and predict various reconstructed sequences.Finally,KELM was used to perform non-linear fitting on the predicted values of the reconstructed sequence,further improve prediction accuracy.The experimental results show that the ICEEMDAN-PE-BiLSTM-KELM model reduces root mean square error(RMSE)by 106.05 MW,mean absolute error(MAE)by 62.34 MW,and mean absolute percentage error(MAPE)by 0.877%compared to traditional long short-term memory(LSTM)models.It can be seen that this model can better solve the complexity and randomness of data while improving prediction accuracy.