基于CEEMD-FE和AOA-LSSVM的短期电力负荷预测

针对电力负荷预测精度不高、效率低的问题，采用算术优化算法(AOA)和最小二乘支持向量机(LSSVM)的模型对经过互补集合经验模态分解(CEEMD)和模糊熵(FE)综合处理后的子序列进行预测，构建了CEEMD-FE-AOA-LSSVM预测模型。首先，利用FE算法对经过CEEMD处理后的各子序列进行熵值重组，该过程提高了模型的抗干扰能力和运算效率。然后，用AOA-LSSVM模型对处理后的子序列进行预测，并将预测叠加输出。最后，通过误差函数对模型进行横向对比和纵向对比，利用两种对比结果来检验其性能。通过实验可知，与CEEMD-LSSVM、AOA-LSSVM、CEEMD-AOA-LSSVM等其他模型相比，CEEMD-FE-AOA-LSSVM组合模型能够兼顾到预测精度与预测效率两方面，做到了综合性能的提升。同时也验证了经过CEEMD或AOA处理的模型能够有效地提升预测精度。

Short-term load forecasting based on CEEMD-FE-AOA-LSSVM

In view of the low accuracy and low efficiency of load forecasting, sub-sequence undergoing processing of complementary ensemble empirical mode decomposition (CEEMD) and fuzzy entropy (FE) is predicted using a model of arithmetic optimization algorithm (AOA) and least squares support vector machines (LSSVM). The CEEMD-FE-AOA-LSSVM prediction model is then constructed. First, the FE algorithm is used to reconstruct the entropy value of each sub-sequence after CEEMD processing. This improves the anti-interference ability and computing efficiency of the model. Then, the AOA-LSSVM model is used to predict each sub-sequence after comprehensive treatment, and the prediction is superimposed on the output. Finally, the model is compared horizontally and longitudinally by the error function, and its performance is tested using the two comparison results. Through experiments, compared with CEEMD-LSSVM, AOA-LSSVM and CEEMD-AOA-LSSVM, CEEMD-FE-AOA-LSSVM combination model can take into account both prediction accuracy and efficiency, and improve the overall performance. At the same time, it is verified that the model processed by CEEMD or AOA can effectively improve prediction accuracy. This work is supported by the National Natural Science Foundation of China (No. 51807133).