Rural Power System Load Forecast Based on Principal Component Analysis

Power load forecasting accuracy related to the development of the power system. There were so many factors influencing the power load, but their effects were not the same and what factors played a leading role could not be determined empirically. Based on the analysis of the principal component, the paper forecasted the demands of power load with the method of the multivariate linear regression model prediction. Took the rural power grid load for example, the paper analyzed the impacts of different factors on power load, selected the forecast methods which were appropriate for using in this area, forecasted its 2014-2018 electricity load, and provided a reliable basis for grid planning.

Short-Term Financial Time Series Forecasting Integrating Principal Component Analysis and Independent Component Analysis with Support Vector Regression

Financial time series forecasting could be beneficial for individual as well as institutional investors. But, the high noise and complexity residing in the financial data make this job extremely challenging. Over the years, many researchers have used support vector regression (SVR) quite successfully to conquer this challenge. In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. Experiments were carried out based on 16 years’ historical data of three prominent stocks from three different sectors listed in Dhaka Stock Exchange (DSE), Bangladesh. The predictions were made for 1 to 4 days in advance targeting the short term prediction. For comparison, the integration of PCA with SVR (PCA-SVR), ICA with SVR (ICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Experimental results show that the proposed model (PCA-ICA-SVR) outperforms the PCA-SVR, ICA-SVR and single SVR methods.

Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks

With the rapid growth of satellite traffic, the ability to forecast traffic loads becomes vital for improving data transmission efficiency and resource management in satellite networks. To precisely forecast the short-term traffic loads in satellite networks, a forecasting algorithm based on principal component analysis and a generalized regression neural network （PCA-GRNN） is proposed. The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic. Specifically, it selects optimal time series of spatio-temporally correlated historical traffic from satellites as forecasting inputs and applies principal component analysis to reduce the input dimensions while preserving the main features of the data. Then, a generalized regression neural network is utilized to perform the final short-term load forecasting based on the obtained principal components. The PCA-GRNN algorithm is evaluated based on real-world traffic traces, and the results show that the PCA-GRNN method achieves a higher forecasting accuracy, has a shorter training time and is more robust than other state-of-the-art algorithms, even for incomplete traffic datasets. Therefore, the PCA- GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper,we propose a hybrid forecasting model(HFM)for the short-term electric load forecasting using artificial neural network(ANN),discrete Fourier transformation(DFT)and principal component analysis(PCA)techniques in order to attain higher prediction accuracy.Firstly,we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation.Approximate curves,together with other input variables,are given to the ANN to predict the next-day hourly load curves.Furthermore,we predict PCA scores to obtain approximate load curves in the first step,which are then given to the ANN again in the second step.Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads.Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018,we train our models using historical data since January 2008.The forecast results show that the HFM consisting of“ANN with DFT”and“ANN with PCA”predicts next-day hourly loads more accurately than the conventional three-layered ANN approach.Their corresponding mean average absolute errors show 2.7%for ANN with DFT,2.6%for ANN with PCA and 3.0%for the conventional ANN approach.We also find that in May,when electric demand is smaller with smaller fluctuations,forecasting errors are much smaller than January for all the models.Thus,we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir.This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir.The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity,extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity.This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multifactors and complex mechanism.The study result shows that this method is a practical,effective,accurate and indirect productivity forecast method and is suitable for field application.

Multivariate Cluster and Principle Component Analyses of Selected Yield Traits in Uzbek Bread Wheat Cultivars

Investigation of genetic diversity of geographically distant wheat genotypes is a useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues > 1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was the greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18,373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).

Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition

Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.

基于KPCA-CNN-DBiGRU模型的短期负荷预测方法

本文针对已有神经网络模型在短期负荷预测中输入维度过高、预测误差较大等问题,提出了一种结合核主成分分析、卷积神经网络和深度双向门控循环单元的短期负荷预测方法。先运用核主成分分析法对原始高维输入变量进行降维,再通过卷积深度双向门控循环单元网络模型进行负荷预测。以第九届全国电工数学建模竞赛试题A题中的负荷数据作为实际算例,结果表明所提方法较降维之前预测误差大大降低,与已有预测方法相比也有大幅的误差降低。

机场能见度临近预测方法

能见度是保障机场航班安全、正常运行的重要标准之一。为精准预测能见度,使用2020年天津机场气象和常规空气质量监测数据,构建基于方差膨胀因子(Variance Inflation Factor,VIF)、主成分分析(Principal Components Analysis,PCA)和Informer的能见度预测模型,并将均方根误差、平均绝对误差、平均绝对百分比误差作为评价指标进行误差分析。结果显示,VIF PCA Informer模型比单一的Informer和简单组合模型效果更优,能更好地捕捉长时间序列特征的关系。相比于单一的Informer、长短期记忆神经网络和门控循环单元模型,VIF PCA Informer模型均方根误差下降了0.2141~0.3486,平均绝对误差下降了0.1842~0.2753,平均绝对百分比误差下降了0.3224~0.5270;VIF PCA Informer模型对能见度的临近预测(1 h)更为精准。使用高效的机场能见度预测模型可在保障航班安全高效运行方面发挥较大支撑作用。

基于GT双标图对小麦新品系的分类评价

为了从产量、品质方面准确评价旱肥地试验小麦新品系,以2020—2021年连续2年参加国家黄淮冬麦区旱地组多点比较试验的26份新品系为材料,应用多元统计方法分析了大田条件下2年间产量和品质性状的变化。结果表明:26份品系的产量和品质性状2年变异系数分别为2.0%~74.2%和2.1%~95.1%,变异较大,变异系数大小顺序依次为稳定时间>湿面筋含量>蛋白质含量>吸水率=单位面积产量>容重;相关分析表明,2年产量与品质性状存在负相关关系,稳定时间均与蛋白质含量、吸水量正相关,稳定时间与产量负相关,其中稳定时间与蛋白质含量相关系数均较高。在相关分析的基础上,采用聚类分析方法将2年中26份小麦参试品系聚为4类,并在主成分品种、性状(genotype by trait,GT)双标图(biplot)和聚类图中进行展示,聚类结果与新品系的实际表现一致,其中‘泰科麦4835’‘洛旱35’‘农大162’‘山农611436’连续2年划为同一类型,表现为产量较高、品质优良。该研究结果可为参试新品系的合理评价和推广应用提供理论依据。

河南省联合体试验小麦品种综合性状分析与评价

主要农作物品种审定联合体试验是当前我国农业形势下拓宽作物品种审定试验渠道重点推进的新措施,为深入了解河南省小麦联合体试验容量、掌握其品种农艺性状和品质性状概况,以近3 a(2021—2023年)河南省审定通过的小麦品种为对象,将联合体试验品种与省统一试验品种进行对比分析和评价。结果表明,2021—2023年河南省通过审定的小麦品种合计355个,其中,联合体试验品种187个。联合体试验容量呈上升趋势,品种数量占比由2021年的48.3%上升到2023年的61.2%;试验组别单一,主要为冬水组。冬水组小麦中,申请者独立申请品种时,联合体试验品种中高校和科研院所占比较省统一试验高,个人申请则更倾向于参加省统一试验。联合体试验小麦农艺性状与省统一试验相比差异前期大后期小,7个农艺性状中2021年有5个性状差异显著或极显著、2023年有2个性状差异显著;品质性状差异相对较小,但也表现出前期大后期小的规律,7个品质性状中2021年有2个性状差异显著、2023年均不显著。联合体试验品种的农艺性状、品质性状的变异系数及多样性整体上均大于省统一试验。利用主成分分析将14个农艺性状和品质性状转化为7个主成分,其反映了81.51%的原始信息。基于主成分分析的评价结果表明,综合评价得分排名前50位的品种中,联合体试验小麦品种在其全部品种中占比较小,2021、2023年分别有5、14个,分别占当年全部联合体试验品种数量的9.3%、17.9%;省统一试验小麦品种在其全部品种中占比较大,2021、2023年分别有4、5个,分别占当年全部省统一试验品种数量的19.0%、35.7%。综上,河南省小麦联合体试验容量在不断提升,农艺性状和品质性状离散程度较大,品种综合评价得分与省统一试验差距缩小,相关部门未来可根据市场调整优化试验组别,聚焦联合体试验通道问题,推进联合体试验高质量发展。

基于主成分和聚类分析的50份冬小麦材料主要农艺性状评价

【目的】了解冬小麦农艺性状间的关系,探究冬小麦农艺性状对产量的影响,从而筛选高产冬小麦品系。【方法】本研究以50份冬小麦品种(系)为研究对象,对其15个农艺性状进行了遗传变异分析、相关分析、主成分分析和聚类分析。【结果】遗传变异分析表明,供试材料农艺性状存在差异,除生育期变异系数为0.71%外,其他农艺性状变异系数较大,为7.46%~51.68%。产量与穗粒质量(0.935**)、千粒质量(0.740**)、穗粒数(0.616**)呈极显著正相关,与小穗数(0.289*)呈显著正相关,与其他农艺性状相关性不显著。主成分分析结果表明,小穗数、穗粒数、小穗粒数、穗粒质量、千粒质量对冬小麦产量提高有较大的正向作用,株高、不育小穗数对冬小麦产量提高有负作用,有27份参试材料基于15个农艺性状综合表现好。聚类分析结果表明,50份冬小麦供试材料划分为多粒高产型、多粒中产型、少粒低产型3大类群,各类群特征表现明显。【结论】50份冬小麦参试材料在15个农艺性状上具有丰富的多样性,通过综合评价筛选出了21份综合性状表现优良的冬小麦材料,可以从中选育出具有高产潜力的冬小麦新品种,同时为冬小麦杂交选育提供了优良的亲本资源和理论参考。142-3-3-1(D38)、141-1-8-1(D35)和2012Y-01-6-1(D43)3份材料较对照陇麦844综合表现优良,产量高。

白条猪价格预测模型构建

【目的】增强农产品价格预测准确度,为农产品价格的有效预测提供参考。【方法】以河南省白条猪每周平均批发价格为研究对象,提出一种基于序列分解、主成分分析和神经网络(CEEMDAN-PCA-CNN-LSTM)的白条猪价格预测方法。首先,使用自适应白噪声完全集合模态分解方法(CEEMDAN)对白条猪价格序列进行分解;其次,选用皮尔逊相关系数筛选影响价格波动的相关因素;再次,利用主成分分析(PCA)对影响因素及分解得到的子序列降维处理并作为原始价格序列的特征值,并行输入到作为编码器的卷积神经网络(CNN)中进行特征提取;最后,引入长短期记忆网络(LSTM)作为解码器输出得到预测结果。将该方法应用于河南省白条猪每周平均价格数据,与LSTM、门控循环单元(GRU)、CNN、基于卷积的长短期记忆网络(ConvLSTM)模型进行比较。【结果】CEEMDAN-PCA-CNN-LSTM组合模型预测方法得到的平均绝对误差分别降低了44.95%、27.30%、28.13%、43.17%。【结论】CEEMDAN-PCA-CNN-LSTM模型对于河南省白条猪市场价格的预测性能更优,有助于相关部门针对河南省白条猪价格波动做出科学决策。

基于主成分分析-BP神经网络的风电备件需求预测

风电机组具有结构复杂,运维困难,且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数,以确保风电场的平稳、高效运行。构建主成分分析-反向传播(principal component analysis-back propagation,PCA-BP)模型,针对受多因素影响的复杂备件,先利用PCA将影响风电备件的要素进行筛选,再利用BP神经网络算法,得到最为精确的预测结果。比较自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明:PCA能显著降低神经网络预测误差,预测的精度为93.94%,高于BP神经网络预测的88.39%和ARIMA模型的85.31%,所以PCA-BP神经网络模型的预测精度准确且有可靠结果,能够适用于风机备件的需求预测。

基于模态分解及GRU-XGBoost短期电力负荷预测

精确的短期电力负荷预测能有效提高电力系统运营水平。针对电力负荷数据受多种因素影响,波动性和随机性强等问题,提出了一种基于模态分解及混合模型的负荷预测方法。首先,采用主成分分析法(principal component analysis,PCA)对负荷特征向量进行处理,去掉冗余信息,再用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将历史负荷分解为简化的几个子序列;其次,选择引入样本熵(sample entropy,SE)来计算子序列熵值,将相近的子序列重构得到随机、细节、低频和趋势分量后选用不同结构门控循环单元(gate recurrent unit,GRU)对不同分量类型进行预测,再使用极致梯度提升模型(extreme gradient boosting,XGBoost)对各分量残差进行拟合,各重组序列的预测值为GRU预测值与XBGoost拟合值之和,重组各序列得到最终预测值。选取3年时电力负荷数据进行实验,结果表明,所提模型的均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolutepercentage error,MAPE)和平均绝对误差(mean absolute error,MAE)分别为370.676 MW、99.07%和246.89 MW,与单一模型和混合模型相比,实现了评价指标的明显减少。

基于经验模态分解和深度学习的短期风电功率预测

精准的风电功率预测有利于全网电力平衡、系统安全稳定运行和节能减耗。提出一种基于经验模态分解(empirical mode decomposition, EMD)、核主成分分析(kernel principal component analysis, KPCA)和长短期记忆(long short-term memory, LSTM)神经网络的短期风功率预测模型。采用EMD技术将多维气象序列分解为多个固有模态分量,以挖掘原始数据的主要特征并消除噪声;引入KPCA进行降维处理,提取数据的非线性特征;使用LSTM神经网络对特征提取的序列进行学习并完成预测,获得风电功率预测的最终结果。使用所提出的模型对新疆某一风电场风电功率进行预测,将预测结果与其他模型对比。结果表明,该预测模型能改善预测性能,降低风电功率预测误差。

小麦粉特性与生湿面品质的关系研究

研究了小麦粉特性与生湿面品质的关系。以13种小麦粉为原料,测定其蛋白质质量、溶剂保持力、内源酶活力等特性,并对制得生湿面的外观、质构及感官品质进行分析。结果表明,麦谷蛋白大聚体质量分数与面片亮度呈显著负相关,谷醇比与熟面黏聚性呈显著负相关,乳酸溶剂保持力与咀嚼性、感官评分呈显著正相关,脂肪氧合酶活力与硬度、咀嚼性呈显著正相关。经主成分分析和聚类分析,将13种小麦粉分为3类:第Ⅲ类小麦粉的蛋白质质量、面团流变学品质显著优于其他小麦粉,熟面食用品质最佳;第Ⅱ类小麦粉制得面片亮度显著高于其他小麦,外观品质最优。通过将国产小麦与第Ⅱ类/第Ⅲ类小麦搭配可用于制备外观与食用品质俱佳的生湿面专用粉。

小麦面筋蛋白遗传特性综合评价

本研究对44个小麦品种的19个面筋蛋白性状进行分析,运用主成分分析、相关性分析、隶属函数法和逐步回归等方法对面筋蛋白品质进行综合评价。结果表明:二硫键含量变幅为3.28~6.78μmol/g,巯基含量的变幅为13.30~16.66μmol/g。面筋蛋白性状变异范围在4.74%~89.46%,其中稳定时间、粉质质量指数等变异系数较大,表明蛋白质量性状的变异潜力较丰富。主成分分析将19个蛋白性状转换为3个综合因子,贡献率分别为40.80%、19.86%和15.88%,累计贡献率达到76.54%,可以很好地将面筋蛋白归为质量性状、聚集性状和数量性状3类。利用隶属函数法计算面筋蛋白综合评价值(D值),主成分因子中载荷较大的12个性状的平均值与D值均呈显著正相关。通过逐步回归建立了7个性状(二硫键含量、麦谷蛋白大聚体含量、蛋白质含量、干面筋含量、面筋指数、拉伸面积和最大峰值时间)作为自变量的回归方程。面包品质评价指标与D值之间具有较好的线性关系,决定系数R2在0.5614~0.7713之间,验证了D值的准确性和可行性。结论:不同筋力小麦面筋蛋白存在较丰富的变异潜力;采用多元统计分析方法综合评价面筋蛋白品质具有可行性;面筋蛋白的量与质以及面筋网络中二硫键的含量可作为小麦品质评价和改良的关键指标。

酿酒专用小麦大曲中挥发性风味成分与微生物群落相关性分析

为研究制曲原料对大曲中挥发性风味成分和微生物群落的影响,以酿酒专用小麦大曲为研究对象,普通小麦大曲为对照,分别采用顶空固相微萃取-气质联用法和高通量测序技术分析大曲的挥发性风味成分和微生物群落组成,并基于多元统计分析方法探讨二者相关性。结果表明,两种小麦大曲共检出125种挥发性风味成分,共筛选出49种差异风味成分,酿酒专用小麦大曲中差异挥发性风味成分种类及含量优于普通小麦大曲。在微生物群落组成上共检出优势细菌属11种、优势真菌属4种;相关性分析表明,乳杆菌属(Lactobacillus)和曲霉菌属(Aspergillus)与醇类含量呈显著正相关(P<0.05),芽孢杆菌属(Bacillus)与吡嗪类含量呈高度显著正相关(P<0.001),泛菌属(Pantoea)、明串珠菌属(Leuconostoc)和葡萄球菌属(Staphylococcus)是醛类主要贡献微生物。采用酿酒专用小麦制曲有益于大曲中细菌的富集培养及挥发性风味成分的丰富和均衡。