基于DBO-VMD和IWOA-BILSTM神经网络组合模型的短期电力负荷预测

Short-term power load prediction based on DBO-VMD and an IWOA-BILSTM neural network combination model

新能源在现代电力系统中占比不断提高,其负荷不规律性、波动性远大于传统电力系统,这就导致负荷预测精度不高。针对这个问题,提出了蜣螂优化(dung beetle optimizer,DBO)算法优化变分模态分解(variational mode decomposition,VMD)与改进鲸鱼优化算法优化双向长短期记忆(improved whale optimization algorithm-bidirectional long short-term memory,IWOA-BILSTM)神经网络相结合的短期负荷预测模型。首先利用DBO优化VMD,分解时间序列数据,并根据最小包络熵对各种特征数据进行分类,增强了分解效果。通过对原始数据进行有效分解,降低了数据的波动性。然后使用非线性收敛因子、自适应权重策略与随机差分法变异策略增强鲸鱼优化算法的局部及全局搜索能力得到改进鲸鱼优化算法(improved whale optimization algorithm,IWOA),并用于优化双向长短期记忆(bidirectional long short-term memory,BILSTM)神经网络,增加了模型预测的精确度。最后将所提方法应用于某地真实的负荷数据,得到最终相对均方根误差、平均绝对误差和平均绝对百分比误差分别为0.0084、48.09、0.66%,证明了提出的模型对于短期负荷预测的有效性。

The share of renewable energy in modern power systems is increasing,causing its load to fluctuate more erratically than in conventional power systems.This volatility leads to lower accuracy of load prediction.To address this issue,this paper introduces a short-term load prediction model combining the dung beetle optimization algorithm(DBO)with optimized variational mode decomposition(VMD)and an improved whale optimization algorithm to optimize bidirectional long short-term memory(IWOA-BILSTM)neural networks.The DBO is used to optimize the VMD,the time series data is decomposed,and various feature data are classified according to the minimum envelope entropy.This enhances the decomposition effect.The fluctuation of the data is reduced by effectively decomposing the original data.Then the whale optimization algorithm is improved using a nonlinear convergence factor,adaptive weight strategy and random difference variation strategy to enhance the local and global search ability of the whale optimization algorithm.Thus an improved whale optimization algorithm(IWOA)is obtained,and it is then used to optimize bidirectional long short-term memory(BILSTM)neural networks,increasing the accuracy of model predictions.Finally,this method is tested on real load data from a location,yielding favorable results.The resulting metrics for relative root mean square,mean absolute and mean absolute percentage errors are recorded at 0.0084,48.09,and 0.66%,respectively.These outcomes verify the effectiveness of the proposed model in short-term load prediction.