多边界条件下热泵利用循环水余热的CPCS-RBF预测控制

循环水余热回收系统中,热泵热网水出口温度在跟踪供热负荷需求时,在受驱动蒸汽量的调节的同时,往往易受热网回水、循环水等工况变化的影响,传统PID控制方式超调量大、负荷跟踪能力差。提出一种混沌变异克隆选择-径向基函数(CPCS-RBF)直接多步预测控制策略,以热泵热网水出口温度预测值与设定值差值为目标函数,利用CPCS优化算法求取目标函数最小时的驱动蒸汽最佳值。预测模型由2个RBF神经网络结合热泵现场运行数据构建,以提高热泵系统适应工况变化的能力;实验结果表明,该控制策略能综合学习热网回水温度、循环水温度等参数的变化,使驱动蒸汽调门超前动作,及时跟踪供热负荷需求变化的同时,适应发电负荷变化下排气余热量的波动,具有更好的节能效果和变工况适应能力。     

基于GRU多步预测技术的云储能充放电策略

为解决云储能日前充放电策略预测的问题,提出了一种基于门控循环单元(GRU)多步预测技术的云储能充放电策略形成方法.首先,鉴于云储能优化需求及单用户负荷预测效果不稳定,构建用户聚类后的GRU多步预测方法预测一天的24点功率.然后分析了云储能模式下的用户和云储能提供商两个主体的交互过程,以预测为基础建立了云储能充放电决策滚动优化模型.仿真算例选取实际数据,在预测聚类用户光伏、负荷功率后,滚动优化求解实际值和预测值下的云储能充放电策略.算例通过5种场景验证了在云储能充放电策略中聚类的作用以及GRU多步预测技术的优势,并且证明云储能模式能够进一步削弱光伏、负荷预测误差的影响.     

一种半隐式的气动弹性时域求解方法

针对结构小幅运动、流动强非线性的气动弹性问题,基于Adams 隐式方法发展了一种半隐式的线性多步法(SILMS)。将隐式的广义气动力项采用显式插值求解,结构项仍采用隐式格式计算,该方法融合了显式方法耦合简单和隐式方法稳定性好的特点。结构运动采用模态坐标描述,流动方程应用计算流体力学(CFD)技术求解,二者采用松耦合方法进行时域推进。算例计算了一个标准气动弹性模型(Isogai wing)的颤振结果,并与几种经典的时域模拟方法进行了比较,证明该方法具有效率高、稳定性好、精度高的优点。     

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiple-branch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-of-the-art framework.     

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.