A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network

Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems.In the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction.The proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and ARIMA.Three real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed model.To verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time series.It is verified that the proposed model has the best performance among all models.

Geometric Mean Decomposition Based Hybrid Precoding for Millimeter-Wave Massive MIMO

Hybrid precoding can reduce the number of required radio frequency(RF)chains in millimeter-Wave(mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition(SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios(SNRs) of different sub-channels. In this paper,we propose a geometric mean decomposition(GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically,we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit(OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.

A High-order Coupled Compact Integrated RBF Approximation Based Domain Decomposition Algorithm for Second-order Differential Problems

This paper presents a high-order coupled compact integrated RBF(CC IRBF)approximation based domain decomposition(DD)algorithm for the discretisation of second-order differential problems.Several Schwarz DD algorithms,including one-level additive/multiplicative and two-level additive/multiplicative/hybrid,are employed.The CCIRBF based DD algorithms are analysed with different mesh sizes,numbers of subdomains and overlap sizes for Poisson problems.Our convergence analysis shows that the CCIRBF two-level multiplicative version is the most effective algorithm among various schemes employed here.Especially,the present CCIRBF two-level method converges quite rapidly even when the domain is divided into many subdomains,which shows great promise for either serial or parallel computing.For practical tests,we then incorporate the CCIRBF into serial and parallel two-level multiplicative Schwarz.Several numerical examples,including those governed by Poisson and Navier-Stokes equations are analysed to demonstrate the accuracy and efficiency of the serial and parallel algorithms implemented with the CCIRBF.Numerical results show:(i)the CCIRBF-Serial and-Parallel algorithms have the capability to reach almost the same solution accuracy level of the CCIRBF-Single domain,which is ideal in terms of computational calculations;(ii)the CCIRBF-Serial and-Parallel algorithms are highly accurate in comparison with standard finite difference,compact finite difference and some other schemes;(iii)the proposed CCIRBF-Serial and-Parallel algorithms may be used as alternatives to solve large-size problems which the CCIRBF-Single domain may not be able to deal with.The ability of producing stable and highly accurate results of the proposed serial and parallel schemes is believed to be the contribution of the coarse mesh of the two-level domain decomposition and the CCIRBF approximation.It is noted that the focus of this paper is on the derivation of highly accurate serial and parallel algorithms for second-order differential problems.The scope of this work does not cover a thorough analysis of computational time.

Modified version of three-component model-based decomposition for polarimetric SAR data

A new hybrid Freeman/eigenvalue decomposition based on the orientation angle compensation and the various extended volume models for polarimetric synthetic aperture radar(PolSAR) data are presented. There are three steps in the novel version of the three-component model-based decomposition.Firstly, two special unitary transform matrices are applied on the coherency matrix for deorientation to decrease the correlation between the co-polarized term and the cross-polarized term.Secondly, two new conditions are proposed to distinguish the manmade structures and the nature media after the orientation angle compensation. Finally, in order to adapt to the scattering properties of different media, five different volume scattering models are used to decompose the coherency matrix. These new conditions pre-resolves man-made structures, which is beneficial to the subsequent selection of a more suitable volume scattering model.Fully PolSAR data on San Francisco are used in the experiments to prove the efficiency of the proposed hybrid Freeman/eigenvalue decomposition.

A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Heterogeneous multicore clusters are becoming more popular for high-performance computing due to their great computing power and cost-to-performance effectiveness nowadays.Nevertheless,parallel efficiency degradation is still a problem in large-scale structural analysis based on heterogeneousmulticore clusters.To solve it,a hybrid hierarchical parallel algorithm(HHPA)is proposed on the basis of the conventional domain decomposition algorithm(CDDA)and the parallel sparse solver.In this new algorithm,a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-nodes,heterogeneous-core-groups(HCGs)and inside-heterogeneous-core-groups through mapping computing tasks to various hardware layers.This approach can not only achieve load balancing at different layers efficiently but can also improve the communication rate significantly through hierarchical communication.Additionally,the proposed hybrid parallel approach in this article can reduce the interface equation size and further reduce the solution time,which can make up for the shortcoming of growing communication overheads with the increase of interface equation size when employing CDDA.Moreover,the distributed sparse storage of a large amount of data is introduced to improve memory access.By solving benchmark instances on the Shenwei-Taihuzhiguang supercomputer,the results show that the proposed method can obtain higher speedup and parallel efficiency compared with CDDA and more superior extensibility of parallel partition compared with the two-level parallel computing algorithm(TPCA).

基于混合分解和PCG-BiLSTM的风速短期预测

为降低风速的随机性对风力发电的影响,提高风速短期预测的精准度,提出一种基于混合分解、双通道输入、多分支PCG-BiLSTM深度学习模型的短期风速预测方法。首先,将全年风速数据分为春、夏、秋、冬4个季度,选取春季作为主要实验对象;其次,利用奇异谱分解(SSD)和变分模态分解(VMD)以降低原始春季风速数据复杂度,生成具有不同模态且复杂度低的子分量,两种不同模式子分量组合为混合分量,实现不同模式分解算法的优势互补;最后,将混合分量以双通道的形式输入到多分支PCG-BiLSTM深度学习模型中,其模型的每个分支由卷积神经网络(CNN)与门控循环单元(GRU)并联组成时空特征提取模块,用于提取两种分解分量组合的混合分量的时空特征,各分支提取对应混合分量的时空特征经聚合后再由双向长短期记忆网络(BiLSTM)进一步提取风速信号的正向和反向双向波动规律,进而得到最终的风速预测结果。多组实验结果表明:提出的组合预测方法在短期风速预测中具有较高的精度和泛化能力,优于其他传统预测方法。

基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别

能源互联网背景下的电能质量问题越来越凸显,针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种混合分量多尺度时频图和残差神经网络(residual neural network,ResNet)、门控循环单元(gated recurrent units,GRU)网络与注意力机制(attention,AT)组合的电能质量复合扰动识别新方法—Res-GRU-AT。首先利用奇异谱分解(singular spectrum decomposition,SSD)和逐次变分模态分解(successive variational mode decomposition,SVMD)对PQDs信号分别进行多尺度分解得到混合分量,再对混合分量进行希尔伯特黄变换(Hilbert-Huang transform,HHT),分析得到多尺度时频图。其次,利用Res-GRU-AT模型对多尺度时频图进行深层次特征提取、强化和识别。Res-GRU-AT模型能够利用ResNet的二维图像空间特征提取能力和GRU的时序特征提取能力进行特征融合,再通过AT进行特征加权强化,提高了PQDs的识别能力。不同方案的仿真结果表明,所提方法特征提取能力强且抗噪性能好,对复合扰动识别率高。

基于改进变分模态分解和优化堆叠降噪自编码器的轴承故障诊断

针对滚动轴承在噪声干扰下故障特征难以提取的问题,提出一种改进变分模态分解(VMD)和复合缩放排列熵(CZPE)的特征提取新方法,并利用优化堆叠降噪自编码器(SDAE)进行故障分类。首先,提出由“余弦相似度—峭度—包络熵”新综合评价指标自适应优化分解参数的改进VMD方法,并通过该指标筛选分解后的本征模态函数(IMF)分量;然后,为提取更全面的故障特征,引入新的复合缩放排列熵对各有效IMF的故障特征进行量化;最后,提出一种基于鼠群优化算法(RSO)与麻雀搜索算法(SSA)的混合算法优化SDAE网络超参数,将故障特征输入优化后SDAE网络中得到分类结果。采用美国CWRU轴承数据集进行验证,实验结果表明该方法能全面稳定地提取背景噪声下的故障特征,且与其他方法相比具有更好的抗噪性能和更高的故障诊断准确率。

运行工况自适应的风储系统双层功率平滑策略

针对风电并网带来的功率波动问题,提出一种运行工况自适应的风电-混合储能系统双层功率平滑策略。在上层,考虑风储系统并网功率波动和储能运行状态,利用无模型自适应控制动态跟踪经滑动平均算法得到的并网参考功率,生成混合储能总功率任务;在下层,考虑储能介质的技术特性,利用模糊控制动态调节经变分模态分解确定的混合储能总功率任务的频率分界点,完成功率的自适应分配。算例分析表明:所提策略可以自适应平抑多工况下的风电功率波动,实现混合储能各介质间功率的合理分配。

基于反相器和开关架构的低复杂度混合预编码方案

采用有少量反相器和开关(Inverter and Switch,IS)组合的预编码架构设计模拟预编码部分,相比传统移相器结构的混合预编码可以有效降低系统功耗。利用此结构的混合预编码在计算收发端最优的编码矩阵时会变成一个求解复杂的离散组合问题。针对多天线多用户的毫米波大规模多输入多输出(Multiple Input Multiple Output,MIMO)场景,提出了一种新的优化方案——SVD_CE,可将2个矩阵的联合优化问题转化为2个独立的组合优化问题,基于改进交叉熵(Cross Entropy,CE)算法分别求解编解码矩阵。仿真结果表明,所提方案与已有解决方案相比不会造成系统性能的损失,在取得相同性能时利用CE算法中所需候选集的数量大幅减少,有效降低了求解的复杂度。

模态分解及混合模型在比特币价格预测中的应用

独特的生产、发行和交易机制等多种因素的影响下,比特币价格表现出极端的波动性,导致了预测任务的复杂性.为此提出了基于改进的自适应噪声完备集合经验模态分解(ICEEMDAN)的混合预测模型,将复杂的原始序列分解成多个简单固有模态函数(IMFs),并通过重构算法将IMFs集成为不同频率的分量.根据各分量的不同数据模式,选取不同机器学习模型分别进行预测,叠加各分量预测结果得到最终比特币价格预测结果.对比结果表明,该模型在各评价指标上均优于单一预测模型,混合模型可以优化预测结果,较好地减小预测误差.

基于改进相关向量机的锂电池剩余使用寿命预测

精确预测锂离子电池剩余使用寿命对于保障设备安全运行十分重要。但电池寿命预测中存在诸如数据噪声和容量再生等不确定性来源,这将导致预测精度大幅下降。为解决这一问题,使用变分模态分解方法对从充电和容量数据中提取的健康因子进行滤波分解,并利用贝叶斯优化方法对相关参数进行优化,提出一种基于多核相关向量机的锂离子电池剩余使用寿命预测模型。利用美国国家航空航天局(NASA)和Oxford电池数据集对所提出的模型进行验证,研究结果表明:所提出的基于变分模态分解和贝叶斯优化的多核相关向量机(VMD-BAYES-HRVM)方法的预测性能不受预测起始点和截止电压的影响,预测结果准确性更高,95%置信区间的跨度更小,证明了所提出方法的有效性。

基于CEEMDAN功率分解的火电厂混合储能容量优化配置

为了解决火电机组跟随自动发电量指令(Automatic generation control, AGC)响应延迟大、超调大等问题,提出一种基于完全噪声辅助聚合经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)的混合储能系统容量优化配置方法。首先,通过某时间段火电机组跟随AGC指令输出曲线,获得混合储能系统需要提供的功率。在此基础上,利用CEEMDAN将需求功率进行分解,获得不同频率下火电机组与AGC指令之间的误差。选择合适的储能元件,构建火电厂响应AGC指令的混合储能系统结构模型,在考虑能量型储能元件磷酸铁锂电池与功率型储能元件飞轮储能系统两类不同储能设备工作特性的情况下进行功率分配。在考虑储能系统荷电状态(State of charge, SOC)、容量与充放电功率等约束下,建立以综合成本最小为目标的容量优化配置模型,将功率分解结果与容量配置模型联合优化,获得最优功率分配情况和对应的储能配置方案。提供工程案例分析,结果表明所提方法可以有效弥补火电机组跟随AGC指令的延迟功率响应,提高火电机组供电可靠性和经济效益,同时相较于单一储能元件,本方案所设计混合储能系统拥有更优经济性。

PARALLEL COMPUTATION OF 3-D HYPERSONIC FLOWS ON UNSTRUCTURED HYBRID MESHES

A parallel virtual machine （PVM） protocol based parallel computation of 3-D hypersonic flows with chemical non-equilibrium on hybrid meshes is presented. The numerical simulation for hypersonic flows with chemical non-equilibrium reactions encounters the stiffness problem, thus taking huge CPU time. Based on the domain decomposition method, a high efficient automatic domain decomposer for three-dimensional hybrid meshes is developed, and then implemented to the numerical simulation of hypersonic flows. Control equations are multicomponent N-S equations, and spatially discretized scheme is used by a cell-centered finite volume algorithm with a five-stage Runge-Kutta time step. The chemical kinetic model is a seven species model with weak ionization. A point-implicit method is used to solve the chemical source term. Numerical results on PC-Cluster are verified on a bi-ellipse model compared with references.

结构动力学有限元混合分层并行计算方法

为减小利用异构众核分布式存储计算机并行求解大规模、超大规模系统结构动力学有限元问题对计算效率造成的损失,在区域分解法的基础上,提出了一种结构动力学有限元混合分层并行计算方法。在该方法中,引入计算过程数据的分布式存储提升数据的内存访问效率。为了弥补传统区域分解法界面方程规模随子区域增加造成通信开销增加的缺点,采用基于网格区域和求解算法区域的混合并行分区,从而降低界面方程的规模,大幅度减少界面方程的求解时间。利用计算过程的三层并行实现计算节点间通信、异构群组间通信与异构群组内通信分离,从而有效提高了通信效率。因此,该方法能够充分利用国产异构众核分布式存储计算机的体系结构特点提升大规模、超大规模系统结构动力学并行计算效率。通过对基准实例的求解,验证了该计算方法的有效性和优越性。

压缩空气与热化学复合储能系统反应器动态性能研究

针对压缩空气与热化学复合储能系统中,由于压缩机的波动运行而导致的反应器不稳定运行问题,通过建立热化学反应过程完备的数理模型,探究了不同工况参数对反应器内工质传热特性和甲醇裂解性能的影响规律,进而建立了反应器的运行调控策略,以实现其内部热能到化学能的高效稳定转化。研究结果表明:在反应器的变工况运行过程中,反应器内反应物和空气温度在气体流动方向分别呈现升高和降低的趋势;甲醇裂解率随空气流量和反应器空气侧入口温度的增加而增加,随甲醇流量的升高而逐渐降低,反应器空气侧入口温度的扰动对甲醇裂解率的影响程度最大,而反应器内甲醇裂解率对甲醇流量扰动的响应速度最快;通过建立前馈加反馈调控策略,分别控制进入反应器和压缩机的工质流量,在压缩机85%~110%的输入功率范围内,反应器可在1000 s内实现95%的甲醇裂解率,证明了所提出调控策略的快速响应性和有效性。

基于CEEMDAN和改进的混合时间序列模型工作面涌水量预测研究

为提高采煤工作面涌水量预测准确度,收集大量工作面涌水量观测数据进行整理、统计、分析,将涌水量稳定性、周期性和季节性特征考虑在内,提出1种基于数据驱动的完全自适应模态分解算法(CEEMDAN)和改进的混合时间序列模型工作面涌水量预测方法。该方法利用CEEMDAN处理涌水量数据,构建麻雀搜索算法(SSA)优化的长短期记忆网络(LSTM)和自回归移动平均模型(ARIMA)并行级联而成的混合时间序列模型对工作面涌水量进行预测。研究结果表明:该模型预测结果与真实数据相差更小,平均绝对误差为6.36 m 3/h,均方根误差为10.6 m 3/h,模型拟合系数为0.95,更适用于工作面涌水量预测。研究结果可为矿井工作面涌水量预测及防控提供参考。

基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法(英文)

提出了一种新的基于一般散射模型的hybrid Freeman/eigenvalue分解算法,用于分析极化合成孔径雷达(PolS AR)数据。文中,单位矩阵作为体散射模型,相干矩阵的两个较大特征值对应的特征向量作为表面散射模型和二次散射模型,并且不需要反射对称条件。新算法有三个优点:第一,表面散射和二次散射不需要反射对称条件,更符合一般散射体的建模;第二,因为散射能量是相干矩阵特征值的线性组合,所以散射能量具有旋转不变性;第三,表面散射能量和二次散射能量避免了负值现象。在San Francisco地区的AIRSAR数据上进行了实验,证明了新算法的有效性。

平抑风电波动的飞轮-电化学混合储能容量优化配置研究

为实现可再生能源的有效集成,减小风电的瞬时功率波动,利用锂电池类能量型储能与飞轮类功率型储能相结合的混合储能系统(HESS)对风电波动进行平抑。首先采用改进k-means算法得到典型日数据,利用经验模态分解(EMD)将其拆解得到HESS平抑任务;在综合考虑多种储能系统功率容量和充放电效率约束的基础上,构建相互协调运行的HESS能量管理系统。此外,以混合储能系统成本与风电功率机会补偿成本最低为目标函数,引入基准线变量和波动惩罚系数进行修正,构建平抑风电波动的HESS容量配置模型。最后结合实际并网数据,得到平滑效果和经济最优的配置方案。结果表明:所提配置方案风电累计欠补偿量共减少了91.8%,经济性提高了49.99%,最佳风储配比为1∶0.16,其中飞轮和锂电池比例为1∶4.65。

基于VMD-WOA混合多尺度分解的区间组合预测模型

针对传统的点预测模型难以适用于随机性复杂系统和非线性非平稳时间序列预测的问题,提出基于VMD-WOA混合多尺度分解的区间组合预测模型。首先,引入基于鲸鱼优化(WOA)的变分模态分解(VMD)混合分解算法,得到最优区间模态子序列;其次,对各区间模态分序列使用指数平滑方法(Holt′s)、支持向量回归(SVR)和BP神经网络预测,得到3个单项预测结果,运用组合预测模型得到模态组合子序列;最后,对模态组合子序列重构,得到最终的区间组合预测序列。为了验证模型的有效性,选取AQI数据进行预测分析,实验表明所提出的基于VMD-WOA的区间组合预测方法具有较高的预测精度和良好的适应性。