本发明公开了一种风机运维数据驱动的LSTMSA神经网络超短期功率预测方法,包括:获得目标风力发电机的运维数据进行预处理;使用lasso算法和皮尔逊相关系数法进行特征筛选,然后训练FNN模型,获得风机发电功率与筛选数据的函数关系;基于FNN模型,使用链式求导法则求得发电功率的变化率;将发电功率的变化率和历史功率值一起作为训练集...
SOC estimation based on the LSTM-SA model In this section, the principles of LSTM and the self-attention mechanism are introduced; the overall architecture of the LSTM-SA model, which is based on the methods mentioned above, is given; and some details of training are presented. Results and ...
针对生物质锅炉燃烧过程的动态特性,提出一种改进的长短期记忆-自注意力机制全卷积神经网络(LSTM-SAFCN)模型用于预测NO_(x)排放浓度.首先利用完全自适应噪声集合经验模态分解法(CEEMDAN)对数据进行预处理,消除数据噪声对NO_(x)排放浓度预测的影响;其次融合自注意力机制与长短时记忆-全卷积神经网络(LSTM-FCN)进行特征...
获取水面漂浮物体轨迹数据集并进行数据预处理,得到水面漂浮物体多维因子融合数据集,水面漂浮物体多维因子融合数据集的多维因子包括纬向速度影响因子和径向速度影响因子;根据预设LSTM神经元选择规则,对LSTM单元进行堆叠并引入空间注意力模块,构建Sa‑LSTM模型;基于Sa‑LSTM模型对水面漂浮物体多维因子融合数据集进行轨迹预测,...
SA-LSTM This project tries to implementSA-LSTMproposed inDescribing Videos by Exploiting Temporal Structure[1],ICCV 2015. Environment Ubuntu 16.04 CUDA 9.0 cuDNN 7.3.1 Nvidia Geforce GTX Titan Xp 12GB Requirements Java 8 Python 2.7.12 PyTorch 1.0 ...
本文利用1991~2020年江西省九江市10个地面气象观测站月降水量实测数据,建立SARIMA、随机森林、LSTM降水量预测模型。结果表明,LSTM模型...展开更多 Monitoring and forecasting precipitation holds great importance in various fields including agriculture, water management, and meteorological disaster warning. This paper...
LSTM的计算公式: 参考Pytorch 循环神经网络: 三. LSTM的变体 peephole LSTM:在计算遗忘门、输入门、输出门时要考虑cell的状态。 耦合遗忘门和输入门:遗忘率和输入率总和为1。 GRU GRU对LSTM做了两个大改动: 将输入门、遗忘门、输出门变为两个门:更新门ztzt(Update Gate)和重置门$r_t$(Reset Gate)。
Using ground-measured global horizontal irradiance dataset, the proposed SA-Bi-LSTM-Bi-GRU model achieves exceptional forecasting performance. It significantly reduces MAE by 64.40–90.34 W/m2, RMSE by 16.15–57.30% W/m2, MAPE by 41.38–69.78%, NMRSE by 8.29–21.65% W/m2 compared to ...
06:32 Model Architecture: LSTM based architecture for question answering. 08:18 Training Overview: Training process, handling question and answer sequences. 10:15 Inference Process: Explanation of answer generation based on questions. In this video I show how we can use what we have learnt so ...
基于BERT-Bi-LSTM-CRF模型的自主式交通系统参与主体识别方法 自主式交通系统(ATS)的重要组成部分是参与主体,参与主体的信息通常依靠文本进行描述.为构建自主式交通知识图谱,需要从文本中准确地识别出大量参与主体.为此,研究了基... 唐进君,庹昊南,刘佑,... - 《交通信息与安全》 被引量: 0发表: 2022年 基于BER...