本发明公开了一种基于HHOLSTM的客户侧柔性负荷预测系统,由四个功能模块组成的,包括:(1)数据分析和预处理模块;(2)基于HHO的模型参数寻优模块;(3)基于LSTM算法的预测模块.(4)对于客户信息和数据集的管理模块.本发明采用HHO对LSTM进行参数寻优,然后使用寻找到的最优参数和对应的模型对客户侧柔性负荷数据进行预测,并且...
最后,我们将HHO-biLSTM模型的预测结果与传统的LSTM模型进行对比分析。 实验结果表明,与传统的LSTM模型相比,HHO-biLSTM模型在风电数据预测中表现出更好的性能。通过引入HHO算法优化LSTM模型,我们可以显著提高模型的准确性和稳定性。此外,HHO-biLSTM模型还能够更好地捕捉风能数据的非线性特征,从而提高了预测的精度。 综...
实验结果表明,与传统的LSTM模型相比,HHO-biLSTM模型在风电数据预测中表现出更好的性能。通过引入HHO算法优化LSTM模型,我们可以显著提高模型的准确性和稳定性。此外,HHO-biLSTM模型还能够更好地捕捉风能数据的非线性特征,从而提高了预测的精度。 综上所述,本文提出了一种基于哈里斯鹰算法优化的长短时记忆HHO-biLSTM模...
CNN-LSTM多监测点水质预测模型中的历史序列长度,卷积核数量,LSTM层神经元数量,隐藏层神经元数量和学习率,并比较3种优化算法的效果.结果显示,Hyperband的运行时间较短,但优化效果较差;在粒子数量和迭代次数设置相同的情况下,哈里斯鹰优化算法优于粒子群优化算法,WT-CNN-LSTM-HHO的RMSE为1.3807,MAE为1.0300,MAPE为...
The hybrid Harris Hawk Optimization (HHO) SVM method adopts the Neuralprophet algorithm to model the seasonality of wind power data and then uses the Pelt technique to the LSTM aided Pelt-Neuralprophet HHO-SVM (i.e., PN-HHO-SVM) scheme, which can utilize the seasonal fluctuations inherent ...
Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the...
Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the...
Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the...
Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the...
Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the...