LSTM网络TPE算法误差校正针对目前区域超短期风电功率预测精度较低且辅助信息不足的问题,提出一种结合贝叶斯优化和长短期记忆(LSTM)神经网络的预测方法.对历史风电功率数据进行数据修复和预处理并搭建LSTM网络模型;依据贝叶斯优化中的TPE算法对模型的超参数寻优,以获得更好的预测性能;为了验证所提出的TPE-LSTM模型的泛化...
Figure 2illustrates the model construction process coupling TPE, STL, and LSTM, as well as their execution logic within the automated monitoring system. The red-framed section in the figure represents the modeling service workflow, while the blue section below illustrates the dam displacement predictio...
这一过程通常包括物理共混和化学改性两种方法。物理共混是通过机械搅拌、熔融共混等方式将各组分均匀混合;而化学改性则是通过化学反应在分子层面上对TPE材料进行改性处理,以进一步提升其性能。 3、成型加工 经过共混与改性后的TPE原料,可以通过注塑、挤出、吹塑、压延等多种成型工艺加工成各种形状和尺寸的产品。这些成型工...
To address the issue of inadequate analysis of factors affecting classroom CO2 levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO2 levels to enhance...
A TPE-STL-LSTM deformation prediction model for concrete dams is established by introducing the TPE algorithm based on the decomposition鈥損rediction model. Taking the Wanjiazhai gravity dam project as an example, a prediction model for the top deformation of 14 dam sections was established and ...