基于相空间重构和进化高斯过程的短期风速预测

提出一种基于相空间重构和进化高斯过程的短期风速预测方法。首先,运用自相关法和假近邻法分别得出原始风速时间序列的延迟时间和嵌入维数,实现混沌风速时间序列的相空间重构;然后,运用进化高斯过程回归模型进行建模,通过高斯过程模型确定输入量和输出量之间的关系,并用改进粒子群算法求取最优超参数。根据某实测风速数据进行了风速预测,结果表明本文所提出的方法能有效提高风速预测精度。     

基于高斯过程回归的网络流量预测模型

针对传统网络流量预测精度低难题,为了获得理想的网络流量预测结果,提出一种基于高斯过程回归(GPR)的网络流量预测模型。该模型首先计算延迟时间和嵌入维数,构建高斯过程回归的学习样本;然后采用高斯过程回归对网络流训练集进行学习,并采用入侵杂草优化对高斯过程回归的参数进行优化;最后采用经典的网络流量测试集对该模型性能进行实验测试。实验结果表明,高斯过程回归模型提高了网络流量的预测精度。     

基于K均值聚类及高斯过程回归集成的铅酸电池荷电状态预测

为了提高铅酸电池荷电状态(State of Charge,SOC)的预测准确率,本文提出一种基于K均值聚类的高斯过程回归集成算法(K-means Cluster with Ensemble Gaussian Process Regression,KC-EGPR)。首先利用K均值聚类算法对原始训练集进行聚类,生成若干个包含原始训练集的某种局部信息的子训练集;然后在每个子集上训练高斯过程回归模型(GPR);最后利用集成学习理论中的自适应提升算法(Ada Boost)对训练的多个GPR进行集成,得到最终的预测模型。在三组铅酸电池数据集上的实验结果表明,所提出的KC-EGPR算法预测铅酸电池SOC的性能优于对比模型,具有广阔的应用前景。     

CEEMD-WT和CNN在短期风速预测中的应用研究

由于风速存在随机性和不稳定性,为了提高短期风速预测的精度,提出了一种基于完备总体经验模态分解（CEEMD）、小波变换（WT）和卷积神经网络（CNN）的短期风速预测混合模型。首先,CEEMD算法把原始风速序列分解成一些相对平稳的固有模态函数和一个残差序列;然后,WT算法对每个固有模态函数进行二次去噪,进一步消除噪声对固有模态函数的影响;最后,卷积神经网络对每个固有模态函数、残差序列和影响风速的5个属性训练预测得到各自的预测结果,对所有的预测结果重构得到最终的预测结果。通过实验与其他4个风速预测模型进行比较,所提出的模型预测的绝对平均百分比误差（MAPE）最小,为2.484%,表明在短期风速预测方面CEEMD-WT-CNN模型有较好的性能。     

一种基于概率关联的局部高斯过程回归算法

在针对控制和机器人的机器学习任务中, 高斯过程回归是一种常用方法, 具有无参数学习技术的优点. 然而, 它在面对大量训练数据时存在计算量大的缺点, 因此并不适用于实时更新模型的情况. 为了减少这种计算量, 使模型能够通过实时产生的大量数据不断更新, 本文提出了一种基于概率关联的局部高斯过程回归算法. 与其他局部回归模型相比, 该算法通过对多维局部空间模型边界的平滑处理, 使用紧凑支持的概率分布来划分局部模型中的数据, 得到了更好的预测精度. 另外, 还对更新预测矢量的计算方法进行了改进, 并使用k-d树最近邻搜索减少数据分配和预测的时间. 实验证明, 该算法在保持全局高斯过程回归预测精度的同时, 显著提升了计算效率, 并且预测精度远高于其他局部高斯过程回归模型. 该模型能够快速更新和预测, 满足工程中的在线学习的需求.     

基于关联规则的PSO-Elman短期风速预测

传统神经网络在短期风速预测中,存在易陷入局部极值和动态性能不足等问题,从而导致风速预测精度较低。为了提高风速预测精度,提出一种基于关联规则的粒子群优化Elman神经网络风速预测模型。利用粒子群算法优化Elman神经网络模型参数,以提高算法的收敛速度,避免陷入局部极值,以得到最优的预测值。同时结合关联规则分析考虑气象因素,采用Apriori算法对风速与其他气象因素进行关联规则挖掘,并利用得到的关联规则对风速预测值进行修正与补偿。实验结果表明,所提出的预测模型的预测效果比传统模型的效果更佳,同时验证了结合关联规则考虑气象因素能够降低风速预测误差。     

复合协方差函数高斯回归的网络流量建模与预测

为了获得更优的网络流量预测结果,提出一种复合协方差函数高斯过程(GP)的网络流量预测模型。首先采用复合协方差函数构建GP模型,然后对网络流量训练集进行训练,找到协方差和均值函数的最优参数,最后建立网络流量预测模型,并与支持向量机、神经网络、传统高斯过程进行网络流量的单步和多步预测对比测试。结果表明,相对于对比模型,复合协方差函数GP模型更加能够辨识非线性的网络流量变化趋势,提高了网络流量的预测精确性,是一种有效的复杂网络流量变化预测方法。     

基于差异补偿和短期采样对比损失的城市电力负荷预测方法

城市电力负荷预测是城市智能电网规划和调度的一项重要内容。然而,城市电力负荷预测中存在数据不均的问题,给城市电力负荷预测带来了巨大挑战。传统的基于单一模型的方法难以解决数据不均的问题,而现有的基于多模型的预测方法根据电力负荷分布将数据集拆分成多个子数据集,然后分别建立多个预测模型进行预测,该类方案在一定程度上解决了数据不均问题,但存在模型构建成本较高、不同分布样本间共有的电力分布特征发生分离等问题。基于此,提出了一个轻量级城市电力负荷预测模型(Lighten-DCSC-LSTM)。该模型通过在长短期记忆网络的基础上引入差异补偿的思想和短期采样对比损失进行构建,同时构建共享特征提取层来降低模型构建成本。其中,差异补偿思想通过学习不同电力负荷分布样本之间的差异对主序列预测模块的预测结果进行差异补偿,短期采样对比损失通过动态类中心的对比学习损失对模型的训练进行正则化。为了验证模型的性能,进行了参数调优和对比实验。对比实验结果表明,模型在预测电力负荷的任务中取得了良好的性能。     

基于高斯过程模型的异常检测算法

为检测数据中的异常信息,提出基于高斯过程模型的异常检测算法。高斯过程可以根据训练样本从先验分布转到后验分布,对核函数的超参数进行推理,预测输出具有清晰的概率解释。对基于高斯过程模型的异常检测算法进行定义和描述,用Server Computers(电脑服务器)数据进行仿真实验,结合高斯过程先验和回归理论,在实验中选取RBF作为核函数,利用目标类数据的特性构造特征向量集,在TE工业过程时序数据集上验证了该算法的适用性和有效性。     

基于多模型的发酵过程高斯过程回归软测量建模研究（英文）

软测量仪表在实际应用中往往存在预测精度低、缺乏预测精度信息等问题。基于多模型方法的软测量仪表通过子模型来描述局部变化,可以有效提高软测量仪表预测精度。在本研究中,高斯过程回归(GPR)模型因其预测方差能够反映预测精度信息特性,被用于构建局部子模型。同时,基于不确定性推理方法,本文提出了基于高斯过程回归预测方差的多模型融合策略。最后,将所提方法应用于工业红霉素发酵过程数据。结果表明,与其他高斯过程回归方法相比较,所提出方法预测精度更高,95%置信区间范围更小。     

基于混核LSSVM的批特征风功率预测方法

针对风电场风功率预测问题, 利用历史风功率、气象数据和测风塔实时数据等相关信息, 提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine, HKLSSVM)方法, 建立风电场风功率预测模型.为了增强模型的适应性, 设计改进的差分进化算法对模型参数进行优化, 并利用稀疏选择方法来选取合适的训练样本集, 缩短建模时间, 保证预测模型精度.根据风场风机的地理位置分布情况, 提出批划分的建模策略, 对相近地理位置的风机进行组批, 替代传统风场风功率预测方法.通过风场中实际数据进行测试, 实验结果表明与其他预测方法相比, 本文提出的方法能够提高预测精度和效率, 减少风电波动性对电网的影响, 从而提高电网的安全性和可靠性.     

基于JITL的电量预测高斯软测量建模研究

为了解决工业过程受本身结构特征、外界因素等影响而存在严重的非线性和时变性等问题,本文提出了一种基于输入输出综合性相似度指标的即时学习高斯过程软测量建模方法。在该方法中,将样本数据进行归一化处理,首先利用传统的基于距离和角度的相似度指标分别对样本输入输出变量进行相似度计算,进而对相似度进行综合,最后选择出最终的相关样本集,建立高斯过程回归软测量模型,将所提基于输入输出相似度指标的即时学习高斯工程软测量模型应用于城市日用电量数据的预测。研究结果表明,所提出的软测量建模方法可以实现对日用电量数据的高精度预测且预测结果具有较小的误差。因此可表明该方法可在电量预测中具有一定的应用可靠性,可以在电力市场预测分析中得到广泛的应用。     

Prediction interval of wind power using parameter optimized Beta distribution based LSTM model

Prediction interval of wind power (PIWP) is crucial to assessing the economic and safe operation of the wind turbine and providing support for analysis of the stability of power systems. The hybrid model (Beta-PSO-LSTM) of long short-term memory (LSTM) neural network and Beta distribution function based particle swarm optimization (PSO) is put forward for prediction interval of wind power. In order to enhance the performance of the Beta-PSO-LSTM for PIWP in training process, wind power series are divided into different power intervals, and then the Beta-PSO-LSTM is used to estimate each power interval of the original wind power series. Furthermore, based on the analysis of the interval forecasting error information in wind power training data set, Beta distribution model is proposed to get better PIWP, and PSO is used to optimize the parameters of the model. Finally, the proposed Beta-PSO-LSTM model is compared with the Beta distribution optimized by PSO based the BP neural network (Beta-PSO-BP), the normal distribution based LSTM neural network (Norm-LSTM), Beta distribution based LSTM neural network (Beta-LSTM), and Beta distribution optimized by iterative method based LSTM neural network (Beta-IM-LSTM) for PIWP. The simulation results show that the PIWP obtained by the Beta-PSO-LSTM model has higher reliability and narrower interval bandwidth, which can provide decision support for the safe and stable operation of power systems.     

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.     

Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting

Accurate and steady wind speed prediction is essential for the efficient management of wind power factories and energy systems. However, it is difficult to obtain satisfactory forecasting performance because of the characteristics of random nonlinear fluctuations inherent in wind speed variation. Considering the drawbacks of statistical models in forecasting nonlinear time series and the problem of artificial intelligence models easily falling into a local optimum, in this study, we successfully integrate the variable weighted combination theory into a new combined forecasting model that simultaneously consists of three disparate hybrid models based on the decomposition technology. Moreover, the extreme learning machine optimized by the multi-objective grasshopper optimization algorithm is adopted to integrate all the forecasting results derived from each hybrid model to further enhance the forecasting accuracy. In this study, we consider a case study that employs several authentic wind speed data aggregates of Shandong wind farms for an evaluation of the forecasting performance of the proposed combined model. The experimental results reveal that this proposed model surpasses the contrasted benchmark models and is satisfactory for intellective grid programs.     

Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks

This paper introduces the concept and practice of Neural Network architectures for wind speed prediction in wind farms. The wind speed prediction method has been analyzed by using back propagation network and radial basis function network. Artificial neural network is used to develop suitable architecture for predicting wind speed in wind farms. The key of wind speed prediction is rational selection of forecasting model and effective optimization of model performance. To verify the effectiveness of neural network architecture, simulations were conducted on real time wind data with different heights of wind mill. Due to fluctuation and nonlinearity of wind speed, accurate wind speed prediction plays a major role in the operational control of wind farms. The key advantages of Radial Basis Function Network include higher accuracy, reduction of training time and minimal error. The experimental results show that compared to existing approaches, proposed radial basis function network performs better in terms of minimization of errors.     

Short-Term Wind Speed Prediction Using Signal Preprocessing Technique and Evolutionary Support Vector Regression

Short-term wind speed prediction is beneficial to guarantee the safety of wind power utilization and reduce the cost of wind power generation. As a kind of the powerful artificial intelligent algorithms, support vector regression (SVR) has been successfully employed in solving forecasting problems. However, due to the intrinsic complexity and multi-patterns of wind speed fluctuations, it is regarded as one of the most challenging applications for wind speed prediction. To alleviate the influence of complexity and capture these different patterns, this study proposes a novel approach named SIE–WDA–GA–SVR for short-term wind speed prediction, which applies the seasonal information extraction (SIE) and wavelet decomposition algorithm (WDA) into hybrid model that integrates the genetic algorithm (GA) into SVR. First, the proposed approach uses SIE to decompose the original wind speed into seasonal and trend components, and the seasonal indices are calculated by SIE. Second, the proposed approach uses WDA to decompose the trend component into both the approximate and the detailed scales. Third, the proposed approach uses GA–SVR to forecast the approximated and detailed scales, respectively. Then, the prediction values of the trend component can be obtained by integrating the prediction values of the approximated scale into the prediction values of the detailed scale. By integrating the seasonal indices into the prediction values of trend component, we can obtain the final forecasting results of the original wind speed. Moreover, the partial autocorrelation function is used to determine the number of input dimension for the SVR, and the GA is used to select the parameters of the SVR. Four real wind speed datasets are used as test samples to verify the proposed approach. Experimental results indicate that the proposed approach outperforms other benchmark models in four statistical error measures, and can improve the forecasting accuracy of wind speed.     

基于GRNN全信息神经网络的超短期风速预测研究

为了更好地研究风功率预测,风速预测显得至关重要.国内神经网络文献均只表现出了短期风速预测,而对于超短期风速预测的神经网络数学模型却相对稀少.引入了GRNN神经网络,详细说明了该方法的超短期风速预测原理并建立了数学模型;为了使超短期风速预测精度有一个良好的对比性分析,将影响风电输出功率的各NWP(numerical weather prediection)信息(包括风速、风向、气温、气压)进行组合,以国内某风电场2014年5月份的各NWP数据进行算例分析,实验结果表明,GRNN全信息神经网络可以达到很好的预测精度,而且运算网络的稳定性甚优.