基于VMD-ICOA-BiLSTM混合模型的日前电价预测

Day-ahead electricity price forecasting based on the combined VMD-ICOA-BiLSTM model

为了进一步提高购售电市场中日前电价的预测准确度,本文将变分模态分解(VMD)、改进郊狼算法(ICOA)和双向长短期记忆神经网络(BiLSTM)相结合,提供一种新型日前电价预估方案。首先,利用VMD把原始电价数据划分成几个子序列,解决电量序列的非平稳性问题;其次,针对郊狼算法收敛速度慢、优化性能不足的缺陷,将Sobol序列引入郊狼初始化,再将全局最优和局部最优郊狼引入算法的组文化趋势;然后,采用ICOA优化BiLSTM的参数,并构建ICOA-BiLSTM混合预测模型,进行子序列预测;最后,对各子序列的预测结果进行求和,得到最终的预测电价。以丹麦电力市场的数据进行检验,结果表明所提方法具有良好的预测准确度和泛化性能。

In order to improve the prediction accuracy of day-ahead electricity prices in the purchase and sales electricity market,this paper combines variational mode decomposition(VMD),improved coyote algorithm(ICOA)and bi-directional long and short-term memory(BiLSTM)network to propose a novel method for day-ahead electricity price forecasting.Firstly,to address the non-smoothness of the electricity price sequence,VMD is used to decompose the original sequence into several subsequences.Secondly,to address the problems of slow convergence and insufficient optimization performance of the coyote algorithm,the Sobol sequence is introduced into the coyote initialization,and the global optimum and local optimum is introduced into the group culture trend.Then,ICOA is used to optimize parameters of the BiLSTM and build an ICOA-BiLSTM prediction model for each subsequence.Finally,the prediction results of all sequences are superimposed to obtain the final prediction result of electricity price.Experiments are conducted on Denmark electricity market data,and the results show that the proposed method has good forecasting accuracy and generalization ability.