基于PSO优化Bi-LSTM的交通流量预测 针对城市交通流量强随机性的问题,为克服非线性和时变特点的影响,提出了基于粒子群(PSO)优化双向长短时记忆网络(Bi-LSTM)的交通流量预测模型,达到城市交通流量高精度预... 樊冲 - 《微型机与应用》 被引量: 0发表: 2022年 基于改进 LSTM 算法的短时交通流量预测 To improve ...
基于PSO-CNN-LSTM的短期热负荷预测模型 为提高短期供热负荷预测精度,减少供热不均与供需失调所造成的能源浪费,提出一种基于粒子群(Particle swarm optimization,PSO),经验模态分解(Empirical Mode Decomposi... 谢文举,薛贵军,白宇 - 《计算机仿真》 被引量: 0发表: 2024年 基于RF的并行CNN-TGLSTM热负荷预测模型...
通过人工电场算法AEFA优化的长短期记忆网络LSTM模型对各分解分量进行预测并重构预测结果.选取某混凝土坝EX16,EX24测点的变形监测资料开展预测研究.结果表明,所建EEMD-AEFA-LSTM模型的预测精度明显高于AEFA-LSTM,PSO-LSTM,GA-LSTM模型,预测结果的平均绝对误差,均方误差,均方根误差均为最小值,为混凝土坝变形的精确预测...
Compared to the model evaluation metrics and training time of the six combination methods, namely TSMFDE-Optuna-XGBoost, TSMFDE-Optuna-LightGBM, TSMFDE-HPO-CatBoost, TSMFDE-SSA-CatBoost, TSMFDE-PSO-CatBoost, and TSMFDE-BO-CatBoost, the proposed method exhibits significant advantages. Among them,...
共四个输入特征(太阳辐射度 气温 气压 大气湿度),一个输出预测(光伏功率); 预测对象可以是电力负荷、风速、光伏等等时间序列数据集; 信号分解方法VMD可以替为EMD CEEMD CEEMDAN EEMD等分解算法; SSA可以改为PSO GWO AOA GA NGO等等其他优化算法; BILSTM也可以为GRU,LSTM等; 代码注释清楚,可以读取本地EXCEL数据,...
A new short-term load forecasting method of power system based on EEMD and SS-PSO Aiming to the disadvantages of short-term load forecasting with empirical mode decomposition (EMD) such as mode mixing and many high-frequency random compo... Z Liu,W Sun,J Zeng - 《Neural Computing & Applic...
applied sciences Article Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy Qiong Qin 1 , Xu Lai 1,* and Jin Zou 2 1 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, Hubei, China; qinqiong...