基于变分模态分解-排列熵-改进鹈鹕优化算法的长短期记忆网络的短期负荷预测

Short-Term Load Forecasting of Long Short Term Memory Network Optimization Based on Variational Mode Decomposition-Permutation Entropy-Improved Pelican Optimization Algorithm

针对传统的电力负荷预测模型中出现的模态分解混叠、长短期记忆网络参数难以选择等问题,提出一种新的模型,即基于变分模态分解、排列熵组合和改进鹈鹕优化算法的长短期记忆网络模型。首先,利用变分模态分解将电力负荷数据分解为多个复杂程度较低的模态,并利用排列熵对子序列进行重组,降低预测难度;接着,引入Logistic混沌映射、融合柯西变异和反向学习两种策略改进鹈鹕优化算法,提高全局寻优能力;然后利用改进后的鹈鹕优化算法对长短期记忆网络参数进行优化,提高模型的泛化能力和实际操作性;最后,对重组后的子模态分别进行预测并叠加,得到最终预测结果,并使用两份不同地区数据集与多种优化算法预测模型进行比较。实验结果表明,变分模态分解-排列熵-改进鹈鹕优化算法的长短期记忆网络模型具有更高的预测精度和稳定性,可以有效地进行短期电力负荷预测。

Aiming at the problems of modal decomposition aliasing and difficult selection of long short term memory network parameters in the traditional power load forecasting model,this paper proposes a new model,namely the long short-term memory network model based on variational modal decomposition and improved pelican optimization algorithm.Firstly,variational modal decomposition is used to decompose the power load data into multiple modes with low complexity,and the subseries are recombined by permutation entropy to reduce the prediction difficulty.Then,two strategies,logistic chaotic mapping,fusion Cauchy mutation and reverse learning,are introduced to improve the global optimization ability.Then,the improved pelican optimization algorithm is used to optimize the long short term memory network parameters to improve the generalization ability and practical operation of the model.Finally,the reconstituted submodes are predicted and superimposed respectively to obtain the final prediction results and compared with the prediction models of different optimization algorithms.Experimental results show that the long short term memory network model of variational mode decomposition-permutation entropy-improved pelican optimization algorithm has higher prediction accuracy and stability,and can effectively predict short-term power load.