浅析电力系统负荷的混沌预测方法

混沌预测是非线性科学领域的研究热点,其潜力非常巨大。文章简要回顾电力系统负荷预测方法的发展历程,详细分析了目前常用的几种混沌预测方法,其中包括相空间重构等主要思想。     

相空间重构理论在基坑位移预测中的应用

针对目前各种基坑位移预测模型所存在的缺点,基于基坑具有的混沌特性,提出运用相空间重构理论来预测基坑位移预测.工程实例应用结果表明,相空间重构理论应用于基坑位移预测是可行的,并具有较好的预测精度.     

海底开采岩层变形混沌时序重构与安全预警系统研究

对海底金矿床开采过程中不同高度岩层位移进行了监测,对岩层变形时间序列重构相空间,用混沌理论揭示了不同高度岩层位移在相空间中的相点距离演变规律。用神经网络建立了岩层变形相空间相点距离演化预测模型,预测了新立矿区海底开采岩层变形,并建立了海底开采岩层变形安全预警系统。采用梯度下降法与混沌优化方法相结合方法训练神经网络,使神经网络预测模型实现快速训练的同时,避免陷入局部极小,同时提高了模型计算精度。研究表明,岩层变形表现出混沌特征,对其相空间重构后,岩层变形的细微变化特征被放大,其内在规律能得到充分展示,为建立海下开采安全预警系统提供了基础。     

基于混沌SVM与ARIMA的工程造价组合预测

为了解决工程造价预测的时效性问题,针对传统线性时间序列预测模型可靠性不高的缺点,引入混沌相空间重构和支持向量机技术,并将两者耦合组成一种非线性预测模型,再利用ARIMA在整体线性趋势预测方面的优越性,对非线性模型进行修正。混沌SVM和ARIMA预测构成组合模型的两个子过程,将两个子过程的预测结果综合平均即可得到最终预测结果。经实例计算,组合模型比最大Lyapunov指数、ARIMA和只将相空间重构与SVM进行耦合的方法拟合效果好,预测精度高,证明其的确具有线性趋势拟合和非线性波动拟合的双优势。     

交通流量的混沌特性分析及预测模型研究

基于混沌动力系统的相空间重构和非线性系统的Volterra级数,分析交通流量的混沌特性,研究了一种交通流量的自适应预测模型。在合理选取嵌入维数和延滞时间实现交通流量时间序列相空间重构的基础上,应用小数据量法计算重构交通流量时间序列的最大Lyapunov指数,根据该指数值对交通流量的混沌特性进行分析,并采用庞卡莱截面法对分析结果进行验证;构建交通流量的Volterra预测模型,并采用LMS自适应算法对模型系数进行调整。通过对实际采集的高速公路交通流量数据的仿真研究表明,小数据量法能对交通流混沌特性进行准确判别,构建的二阶Volterra自适应预测模型能够有效地预测交通流量的变化。因此,在判定交通流量存在混沌特性时,可以应用论文构建的二阶Volterra自适应预测模型对其进行准确的预测。     

相空间重构与RBF结合的短时交通流预测研究

针对短时交通流的混沌特性,提出将相空间重构与神经网络相结合的预测算法,并采用某高速公路实时交通流数据进行了仿真验证,取得了较好的预测效果。     

瓦斯等级鉴定的混沌相空间重构预测技术研究

矿井瓦斯等级鉴定是确定煤矿属于低瓦斯矿井、高瓦斯矿井还是煤与瓦斯突出矿井以及预防煤矿瓦斯爆炸危险和矿井自然发火危险等的一个重要手段.论文针对柏林煤矿1989-2004年共16年矿井瓦斯等级鉴定的结果,采用了混沌相空间重构技术,提出了瓦斯等级鉴定结果预测的一种新方法,并成功预测了2005年瓦斯等级鉴定结果.证明了这种基于混沌相空间重构技术的新方法处理已有的瓦斯等级鉴定结果,预测来年的绝对和相对瓦斯涌出量是可行、可靠的.     

基于混沌时序预测方法的冲击地压预测研究

冲击地压的预测研究通常是通过监测对冲击地压的发生、发展过程比较敏感的指标来进行的，监测指标值的大小和变化规律是进行预测的基础，监测数据在未来一定时期的峰值变化和走势变化规律对于预测过程具有重要意义。首先，通过对互信息和伪邻近点数的计算，确定观测序列的延迟时间和嵌入维数等相空间重构参数；然后，在对观测序列相空间重构的基础上，运用一阶局域近似法和基于最大Lyapunov指数法等混沌预测方法对冲击地压上作面的观测时间序列进行数学建模，并与传统的数理统计预测方法进行对比分析；最后，用实例对冲击危险区域的电磁辐射序列及顶板下沉速度序列等进行预测运算和分析，其结果表明，运用混沌理论的预测方法可达到较高的预测精度。     

基于SVM的混沌时间序列预测模型应用研究

混沌时间序列预测是混沌理论的一个重要应用领域和研究热点,目前它在信号处理、自动化控制等领域中已得到了广泛的应用。本文联系支持向量机(SVM)和混沌时间序列预测的相关理论,建立基于二者的变形序列预测模型。同时,结合具体实例从变形时间序列的混沌识别、相空间重构以及预测模型的参数优化等方面探讨了模型的具体建立过程。实验结果表明,该模型的预测精度要优于BP神经网络。     

基于相空间重构的BP神经网络对边坡的位移预测研究

边坡的位移预测对其稳定性的预报具有十分重要的意义,从基于相空间重构的BP神经网络预测方法对位移时间序列进行了分析,对相空间重构的参数延迟量以及嵌入维数进行了论述,将预测结果与传统的BP神经网络模型的预测结果进行了比较。结果表明,基于相空间重构的BP神经网络具有更高的精度,是一种优秀的预测方法。     

城市道路网节点短时段交通量预测模型研究

准确的短时段交通量预测在良好的道路交通管理中将越来越成为至关紧要的一个步骤。本文应用BP学习算法及进行误差校正的SPDS算法,建立了基于BP网络的城市道路网节点短时段交通量预测模型。并依据哈尔滨市省政府交叉口2001年6月15日15min间隔的交通量调查数据,对中宣街进行了分时段的交通量预测。本文还对交通量神经网络预测模型提出了五种输入层方案,针对不同输入层方案,采用试算法确定最佳隐层单元数,根据各方案的训练时间和误差进行评价,确定了理想的交通量神经网络预测模型,并分析了输入层单元和隐层单元分别与训练时间和误差的关系。最后,采用确定的交通量预测模型进行预测,预测结果证明了本模型在较短时间内具有较高的预测精度。     

物流园区产生交通量预测的研究

阐述了预测物流园区产生交通量的重要性,探讨了综合型物流园区产生交通量的预测思路,即通过分别预测货运与非货运交通量并叠加获得路网中新增交通量,其中针对货运交通量预测采用了两种不同的思路.     

TransCAD在城市公交模型中的应用

本文以福州市公交客流预测模型为例,阐述了TransCAD建立公交客流预测模型的步骤和方法,着重介绍了如何在TransCAD里如何建立公交线网,自定义路阻函数以及分配公交客流,并就其间常遇到的问题及解决办法进行探讨。     

Temporal Evolution of Short-Term Urban Traffic Flow: A Nonlinear Dynamics Approach

Abstract:   Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified .     

Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis

Abstract:   Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models .     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.     

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.     

交通流的有序运动与混沌运动相互转化现象的仿真研究

用Matlab软件编制皮埃莱(Bieriey)模型来产生仿真交通流。在一定的参数组合下,仿真研究了交通流车队中前后车辆之间的车头间距的变化过程。分析了这种车头间距变化过程的曲线。给出该曲线的二维(间距差与速度差)和三维(间距差、速度差、时间)相图。从相图上可以明显地看出存在奇怪吸引子,这说明基于跟驰模型产生的交通流存在着混沌现象。从相图还可以清楚地看出交通流混沌运动与有序运动之间的转化过程。联系交通流的实际情况,对仿真结果做了分析。