基于CEEMDAN-SE和DBN的短期电力负荷预测

为提高短期负荷预测精度,提出一种基于自适应噪声的完全集合经验模态分解(CEEMDAN)-样本熵(SE)和深度信念网络(DBN)的短期负荷组合预测模型。利用CEEMDAN-样本熵将原始负荷序列分解为多个特征互异的子序列,计算各子序列的样本熵,将熵值相近的子序列重组得到新序列,降低了原始非平稳序列对预测精度造成的影响并减小计算规模;综合考虑各新序列的周期特性和影响因素对每个新序列分别构建不同的DBN预测模型,利用DBN克服了浅层神经网络特征提取不充分及初始参数难确定的问题;最后将预测结果叠加得到最终预测值。仿真结果表明,该组合预测模型的平均绝对百分比误差和均方根误差分别为1.18%和87.91 MW,相比于BP、DBN、EMD-DBN负荷预测模型具有更高的预测精度。

Short-term load forecasting based on CEEMDAN-SE and DBN

To increase the accuracy of short-term load forecasting, a short-term load combination forecasting model based on adaptive noise-based full set empirical mode decomposition (CEEMDAN)-sample entropy (SE) and deep belief network (DBN) is proposed. Firstly, CEEMDAN-sample entropy is used to decompose the original load sequence into multiple sub-sequences with different characteristics. The sample entropy of each sub-sequence is calculated, and the sub-sequences with similar entropy values are recombined to obtain a new sequence, which reduces the accuracy of the original non-stationary sequence. The impact is reduced and the computational scale is reduced. Then, considering the periodic characteristics and influencing factors of each new sequence, different DBN prediction models are constructed for each new sequence,Using DBN prediction model to overcome the problem of insufficient feature extraction and initial parameters of shallow neural network and finally the prediction results are superimposed to obtain the final prediction value. The simulation results show that the combined prediction model effectively improves the prediction accuracy.