基于改进猎人猎物算法的VMD-KELM短期负荷预测

VMD-KELM Short-term Load Forecasting Based on Improved Hunter-prey Optimizer

为进一步提高负荷预测的准确性和可靠性,针对核极限学习机(Kernel extreme learning machine,KELM)参数选择影响预测能力的缺点以及负荷数据的波动性和非平稳性的特征,提出一种基于变分模态分解(Variational mode decomposition,VMD)与经种群混沌策略和莱维飞行策略改进的猎人猎物算法(Levy-hunter-prey optimizer,LHPO)优化KELM的预测模型。首先,使用灰色关联分析对原始数据的环境因素与负荷数据进行相关性分析;然后,使用VMD对负荷数据进行分解,分别将每个分解子序列输入经LHPO优化的KELM模型进行负荷预测;最终,将每个预测结果进行叠加。仿真试验验证该预测模型对短期负荷预测具有较高的适应性。

In order to further improve the accuracy and reliability of load forecasting,a forecasting model based on variational mode decomposition(VMD)and Levy-hunter-prey optimizer(LHPO)improved by population chaos strategy and Levy flight strategy is proposed to optimize kernel extreme learning machine(KELM),in view of the shortcomings of the KELM parameter selection that affects the forecasting ability and the characteristics of the volatility and non-stationary of load data.Firstly,the environmental factors and load data of the original data are analyzed by using the grey relational analysis.Then,VMD is used to decompose the load data,and each decomposition subsequence is input into the KELM model optimized by LHPO for short-term load forecasting.Finally,each prediction result is stacked.Simulation experiment results show that the prediction model has high adaptability to short-term load forecasting.