基于在线支持向量机的锅炉燃烧系统动态建模

通过燃烧优化提高电站锅炉效率并降低NO_x排放,是实现电厂节能减排的重要手段。目前大多数的燃烧优化方法都是基于锅炉燃烧系统的稳态模型,因而难以实现动态变负荷情况下的燃烧优化。针对该问题,提出了一种改进的在线自适应最小二乘支持向量机动态建模算法。该算法首先进行离线的支持向量筛选,不仅减少了建模所需样本数,也确保了支持向量的稀疏性;然后,采用替换、新增、删除3种支持向量的在线更新策略,使算法能够更好地适应对象特性的变化。将上述算法应用于建立某600 MW机组锅炉燃烧系统的动态模型。仿真结果表明,所建模型能够准确反映锅炉效率和NO_x排放随负荷变化的动态特性。相比原有基于在线最小二乘支持向量机建立的稳态模型,其具有更高的精度和预测能力。同时,该模型结构简单、在线计算量小,为进一步研究锅炉燃烧动态优化控制策略奠定了基础。     

基于LSTM的电站锅炉NO_x排放浓度建模研究

为建立机组变负荷运行时NO_x排放浓度与运行参数间的定量模型,以实现燃烧优化降低NO_x排放。考虑到机组运行数据具有的时间序列特征,本文研究了基于深度学习算法长短期记忆网络(Long-short term memory networks, LSTM)的NO_x建模方法。LSTM通过在递归神经网络(Recurrent neuralnetwork, RNN)中引入包含三个门控信号的记忆单元,可在反向训练过程中有效避免梯度消失和爆发问题。基于某1000MW超临界机组变工况运行数据进行了NO_x建模,模型测试结果表明,在选取的模型结构与输入特征下,采用LSTM对历史数据进行学习,可找出参数间的相互影响关系,模型预测结果比传统神经网络及支持向量回归更接近实际值。     

基于UKF-LSSVM的燃煤机组NO_x排放浓度预测方法

目前,燃煤机组广泛采用选择性催化还原(SCR)脱硝技术。对燃煤机组NO_x排放浓度进行准确预测,不但有利于进一步提高SCR控制系统的调节品质,而且可以评估现场所收集到的数据是否真实准确,为环保部门对电厂排放NO_x浓度进行监管执法提供依据。提出一种基于无迹卡尔曼滤波最小二乘支持向量机(UKF-LSSVM)的NO_x排放浓度预测方法。基于现场数据和理论分析,确定了脱硝系统动态模型的输入和输出变量。采用无迹卡尔曼滤波(UKF)不断更新核参数σ和其他模型参数α、b,并采用样本更新策略对支持向量进行更新,提高了模型的自适应能力。将该方法用于某300 MW机组脱硝系统的NO_x排放浓度预测。仿真结果表明,所建模型能够准确预测燃煤机组NO_x排放浓度。与原有基于批量最小二乘支持向量机(LSSVM)建立的稳态模型相比,该方法具有更高的预测精度和自适应能力,为进一步研究脱硝系统动态优化控制奠定了基础。     

基于改进PSO优化SVR的交通事故预测模型

为了有效预测交通事故,提出一种基于改进粒子群算法优化支持向量回归机的预测模型。改进粒子群算法利用网格搜索对全局最优粒子的邻域进行精细搜索,结合粒子群算法较快的收敛速度和网格搜索较强局部搜索能力的优点,提高了支持向量回归机相关参数的优化精度,进而改善了交通事故预测模型的预测性能。仿真结果表明,基于改进粒子群算法优化支持向量回归机的交通事故预测模型达到了较快的学习速度和较高的预测精度,具有良好的工程应用性。      

基于混沌粒子群SVR的烟气轮机状态预测

研究烟气轮机状态测试,为了保证安全,克服当前烟气轮机状态预测精度低的问题,结合混沌粒子群与支持向量回归模型(SVR)的特点,提出一种烟气轮机状态预测的新方法,混沌粒子群能克服粒子群优化算法容易陷入局部最优且收敛速度较慢等缺点,以获得高预测性能的支持向量回归模型.在分析支持向量回归算法和混沌粒子群算法基础上,采用混沌粒子群算法选取合适的支持向量回归模型,并利用训练集建立混沌粒子群SVR烟气轮机状态预测模型.以某烟气轮机机组作为应用对象测试方法在机电状态预测中的效果.实验结果表明,与粒子群SVR相比,混沌粒子群SVR的预测精度有了较大幅度提高,证明适合烟气轮机状态预测.     

基于SVM的大样本数据回归预测改进算法

针对支持向量机回归预测精度与训练样本尺寸不成正比的问题,结合支持向量机分类与回归算法,提出一种大样本数据分类回归预测改进算法。设计训练样本尺寸寻优算法,根据先验知识对样本数据进行人为分类,训练分类模型,基于支持向量机得到各类别样本的回归预测模型,并对数据进行预测。使用上证指数的数据进行实验,结果表明,支持向量机先分类再回归算法预测得到的均方误差达到12.4,低于人工神经网络预测得到的47.8,更远低于支持向量机直接回归预测得到的436.9,验证了该方法的有效性和可行性。     

超超临界锅炉闭环燃烧优化控制方法及应用!

燃烧优化是实现锅炉进一步节能减排的重要手段。针对1 000 MW超超临界锅炉,提出了一种闭环燃烧优化控制方法。首先,采用在线最小二乘支持向量机(LSSVM)建立了燃烧系统的动态模型,以准确描述控制变量、主要扰动与输出参数之间的动态变化关系;然后,结合预测控制的思想,构建了兼顾锅炉效率和NO_x排放的经济性性能指标,并利用非线性优化算法求解滚动优化问题,实现了对配风方式、氧量等运行参数的闭环自动调节;最后,设计、开发了相应的优化控制软件,并成功实现了现场应用。应用结果表明,该方法能够在保证锅炉效率的情况下,明显降低NO_x排放,并有效克服煤种变化和负荷变化的影响。该方法对推动智能控制技术在工业过程中的实际应用具有借鉴意义。     

基于ICOA和SVR的短期负荷预测北大核心

针对短期负荷预测精度与运行时间难以兼顾的问题,提出一种基于改进郊狼优化算法的支持向量回归模型。阐述了支持向量回归模型的原理,分析郊狼优化算法并在郊狼成长方式和贪心选择策略上进行改进。通过利用负荷、天气、日期等数据训练SVR模型,同时采用改进郊狼优化算法选择SVR参数并建立ICOA-SVR负荷预测模型。经算例分析与比较,表明在短期负荷预测方面,ICOA-SVR模型比COA-SVR、BP神经网络和LSTM模型具有更高的预测精度和更快的预测速度。     

超超临界机组锅炉效率与NOx排放的建模研究

锅炉热效率和NOx排放一直是火电机组运行中的重要指标,在大容量、高参数超超临界机组呈现良好发展势头的当前,对1000MW超超临界机组锅炉燃烧系统的建模问题进行研究具有重要意义.借助某电厂1000 MW超超临界锅炉的现场燃烧调整试验数据,分别建立锅炉热效率和NOx排放量的最小二乘支持向量机(LSSVM)模型,采用一种改进的粒子群算法对模型参数进行优化选取.实验结果表明,锅炉热效率和NOx排放量的平均预测误差分别可达到0.78％和4.22％,验证了LSSVM模型的有效性.     

基于支持向量回归机的股票价格预测

研究股票价格预测问题,股票价格变化具有非线性、时变性,传统线性预测模型难以准确刻画股价变化规律,且非线性神经网络存在过拟合、局部最小等缺陷,预测精度比较低。为提高股票价格预测精度,提出一种基于粒子群优化支持向量机的股票价格预测模型。利用粒子群算法良好的寻优能力,对支持向量机参数进行优化,加快支持向量机学习速度,再采用非线性预测能力优异的支持向量机对股票价格进行预测。以南天信息股票价格对模型性能进行仿真,实验结果证明,支持向量机预测模型能全面反映股票价格变化的非线性的时变规律,获得更高预测精度,预测结果对股民实际操作具有较大的指导价值。     

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

Boiler combustion optimization is a key measure to improve the energy efficiency and reduce pollutants emissions of power units. However, time-variability of boiler combustion systems and lack of adaptive regression models pose great challenges for the application of the boiler combustion optimization technique. A recent approach to address these issues is to use the least squares support vector machine (LS-SVM), a computationally attractive machine learning technique with rather legible training processes and topologic structures, to model boiler combustion systems. In this paper, we propose an adaptive algorithm for the LS-SVM model, namely adaptive least squares support vector machine (ALS-SVM), with the aim of developing an adaptive boiler combustion model. The fundamental mechanism of the proposed algorithm is firstly introduced, followed by a detailed discussion on key functional components of the algorithm, including online updating of model parameters. A case study using a time-varying nonlinear function is then provided for model validation purposes, where model results illustrate that adaptive LS-SVM models can fit variable characteristics accurately after being updated with the ALS-SVM method. Based on the introduction to the proposed algorithm and the case study, a discussion is then delivered on the potential of applying the proposed ALS-SVM method in a boiler combustion optimization system, and a real-life fossil fuel power plant is taken as an instance to demonstrate its feasibility. Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.     

基于支持向量机的烟气二氧化硫排放量预测模型

针对循环流化床锅炉控制系统的烟气SO₂对象的非线性特点,本文建立了一种基于支持向量机的烟气SO₂排放量预测模型. 由于直接网格搜索确定支持向量机回归模型参数的方法计算量大、搜索时间长,本文采用单变量参数搜索结合网格寻优的方法来确定模型参数. 仿真结果表明,基于支持向量机方法建立的循环流化床锅炉烟气SO₂排放量预测模型具有良好的预测效果.     

A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler

NOx emissions from power plants pose terrible threat to the surrounding environment. The aim of this work is to achieve low NOx emissions form a coal-fired utility boiler by using combustion optimization. Support vector regression (SVR) was proposed in the first stage to model the relation between NOx emissions and operational parameters of the utility boiler. The grid search method, by comparing with GA, was preferably chosen as the approach for the selection of SVR’s parameters. A mass of NOx emissions data from the utility boiler was employed to build the SVR model. The predicted NOx emissions from SVR model were in good agreement with the measured. In the second stage, two variants of ant colony optimization (ACO) as well as genetic algorithm (GA) and particle swarm optimization (PSO) were employed to find the optimum operating parameters to reduce the NOx emissions. The results show that the hybrid algorithm by combining SVR and optimization algorithms with the exception of PSO can effectively reduce NOx emissions of the coal-fired utility boiler below the legislation requirement of China. Comparison among various algorithms shows the performance of the well-designed ACO outperforms those of classical GA and PSO in terms of the quality of solution and the convergence rate.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

改进PSO优化参数的LSSVM燃煤锅炉Nox排放预测

为了提高燃煤锅炉NOX排放浓度预测的准确度,更好地进行氮氧化物的污染监测,提出了一种结合最小二乘支持向量机(Least squares support vector machines,LSSVM)和改进的粒子群优化算法(Particle swarm optimization,PSO)的预测方法。依据LSSVM预测原理及其参数选择的不确定性,采用一种改进的PSO优化算法对模型参数进行寻优,建立锅炉燃烧NOX排放特性模型,并与另两种方法结果进行比较。结果表明:LSSVM是一种有效的建模方法,有较高的拟合度;改进的PSO与LSSVM结合可改善模型的预测精度和泛化能力,在NOX排放浓度预测方面明显优于其他两种参数优化算法,对NOX排放预测有指导意义。     

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

In order to reduce the energy consumption in the cloud data center, it is necessary to make reasonable scheduling of resources in the cloud. The accurate prediction for cloud computing load can be very helpful for resource scheduling to minimize the energy consumption. In this paper, a cloud load prediction model based on weighted wavelet support vector machine(WWSVM) is proposed to predict the host load sequence in the cloud data center. The model combines the wavelet transform and support vector machine to combine the advantages of them, and assigns weight to the sample, which reflects the importance of different sample points and improves the accuracy of load prediction. In order to find the optimal combination of the parameters, we proposed a parameter optimization algorithm based on particle swarm optimization(PSO). Finally, based on the WWSVM model, a load prediction algorithm is proposed for cloud computing using PSO-based weighted support vector machine. The Google cloud computing data set is used to verify the algorithm proposed in this paper by experiments. The experiment results indicate that comparing with the wavelet support vector machine, autoregressive integrated moving average, adaptive network-based fuzzy inference system and tuned support vector regression, the proposed algorithm is superior to the other four prediction algorithms in prediction accuracy and efficiency.     

利用PSO-SA混合优化支持向量回归的径流预报模型研究

为了有效提高径流预报的准确度,提出一种有效的融合优化策略,采用基于粒子群和模拟退火算法相结合的混合方法同时优化支持向量回归核函数类型和内核参数,以此建立一种有效的混合优化支持向量回归径流预报模型。提出的方法为核函数选择和参数优化提供了一种有效途径。通过对广西柳州柳江径流实例分析,并与纯粹的支持向量回归模型对比,研究结果表明,该模型预测稳定,具有较高泛化性能和预测准确度,为径流预报提供了一种有效预测方法。     

针对小规模数据集的多模型融合算法研究

目前,对小规模数据集进行预测时,主要使用传统机器学习算法,但传统单一模型预测效果不能达到预期准确率,且无法兼顾多项评价指标。因此,文中以小规模数据集为研究对象,融合决策树、逻辑回归、支持向量机三类模型,提出了一种多模型融合算法,并分析了其在小规模数据集上的应用效果。首先,简述了决策树、逻辑回归和支持向量机的算法原理;其次,使用决策树、逻辑回归和支持向量机作为基学习器并完成单独训练,将各模型输出结果用于下一阶段模型输入,同时使用最大似然估计迭代优化参数,从而完成多模型融合过程;最后,对数据集进行分析和处理,通过实验与单一模型进行指标对比。实验结果表明,多模型融合算法在预测精确率、召回率、准确率等方面有明显提升。     

铁水硅含量的混沌粒子群支持向量机预报方法

提出一种基于混沌粒子群优化(CPSO)的支持向量回归机(SVR)参数优化算法, 并使用该算法建立高炉铁水硅含量预测模型(CPSO–SVR), 对某大型钢铁厂高炉铁水硅含量的实际采集数据进行预测, 结果表明基于混沌粒子群优化算法寻优的参数建立的铁水硅含量支持向量回归预测模型具有良好的预测效果. 与最小二乘支持向量回归机(LS–SVR)、使用粒子群优化算法训练的神经网络(PSO–NN)进行比较, CPSO–SVR模型对铁水硅含量进行预测时预测绝对误差小于0.03的样本数占总测试样本数的百分比达到90%以上, 预测效果明显优于PSO–NN, 且比LS–SVR稳定性更强, 可用于高炉铁水硅含量的实际预测, 表明混沌粒子群优化算法是选取SVR参数的有效方法.     

改进PSO-ISVM算法的软件缺陷预测

提出基于改进的粒子群优化支持向量机方法（PSO-ISVM）的测控软件缺陷预测方法。通过引入代价惩罚系数,定义粒子群优化算法中的适应度函数,利用最小化适应度函数值作为优化目标,排除大量的冗余干扰信息,提高对测控软件有缺陷模块的预测准确度,寻找支持向量机的最优参数。通过仿真实例分析测控软件有效性,并与常用缺陷预测方法进行比较,表明该模型能加快软件缺陷预测速度和提高对有缺陷模块的预测准确度。