Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

At present,the large-scale access to electric vehicles(EVs)is exerting considerable pressure on the distribution network.Hence,it is particularly important to analyze the capacity of the distribution network to accommodate EVs.To this end,we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load.First,the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed.Second,the charging behavior of an EVis simulated by combining the Monte Carlo method and the trip chain theory.After obtaining the temporal and spatial distribution of the EV charging load,themethod of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network.By adjusting the EV ownership,the EV capacity in the distribution network is analyzed and solved on the basis of the following indices:node voltage,branch current,and transformer capacity.Finally,by considering the 10-kV distribution network in some areas of an actual city as an example,we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

Endpoint forecasting on composite regeneration by coupling cerium-based additive and microwave for diesel particulate filter

Numerical simulation has been carried out to investigate the major factors affecting the time of composite regeneration due to coupling cerium-based additive and microwave for diesel particulate f3ilter(DPF). Effect on the composite regeneration time from various factors such as mass flow rate of exhaust gas, temperature of exhaust gas, oxygen concentration of exhaust gas, microwave power and amount of cerium-based additive are investigated. And a mathematical model based on fuzzy least squares support vector machines has been developed to forecast the endpoint of the composite regeneration. The results show that the relative error of endpoint forecasting model of composite regeneration is less than 3.5%, and the oxygen concentration of exhaust gas has the biggest effect on the endpoint of composite regeneration, followed by the mass flow rate of exhaust gas, the microwave power, the temperature of exhaust gas and the amount of cerium-based additive.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Research on the Geological Sourcing of Raohe Honey by Inductively Coupled Plasma Mass Spectrometry with Primary Composite Analysis and Forecasting Models

Raohe honey (Honey in Raohe) is the only product which has obtained China’s national geographical mark for honey;however, it is always counterfeited by some producers due to its excellent quality. In this research, Raohe honey was identified by geographical sourcing, where the detection on 166 Raohe honey samples and 31 non-Raohe honey samples was conducted with Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Additionally, the method of Primary Composite Analysis accomplished dimensionality reduction by transforming the abundance ratios variables of 13 isotopes to 4 primary composites, and could explain 91.17% of the total variables. There were five models: Decision Tree, Naive Bayes, Neural Network, Partial Least Square Discriminate and Support Vector Machine, built on the four new variables of primary composites with the Agilent MPP Software. The validation of the models was performed with 11 Raohe honey samples and 5 non-Raohe honey samples randomly selected. The accuracies of the Decision Tree and Support Vector Machine models were both 93.97%, and those of the Naive Bayes and Neural Network models were both 87.5%, while the contribution rate of the Partial Least Square Discriminate model was only 75%. It was concluded that the Decision Tree and Support Vector Machine models could be used for indentifying Raohe honey, and the Naive Bayes and Neural Network models could work as references, while the Partial Least Square Discriminate model was not suitable for identifying Raohe honey.

基于VMD-CNN-GRU-LSTM组合模型的汽车销量预测分析

针对汽车销量时间序列数据的季节性、非线性性、非平稳性等复杂特征,提出一种融合变分模态分解(Variational Mode Decomposition,VMD)的卷积神经网络(Convolutional Neural Network,CNN)、门控循环单元(Gated Recurrent Unit,GRU)和长短期记忆(Long Short-Term Memory,LSTM)神经网络组合的汽车销量预测方法,通过VMD将汽车销量时序数据进行分解,利用CNN提取关键特征,并通过GRU与LSTM捕捉汽车时序数据的时间依赖关系.实验表明该方法有较好的预测性能.

基于负荷预测的中低压母管压力快速协调控制系统分析

阐述一套快速响应系统的设计,介绍机组正常运行和当机组出现故障时的响应情况。当机组出现故障时,可根据主蒸汽压力、关口压力及其变化率等做出响应,确保关口供汽压力温度波动稳定。

复合型轨道交通车站交通接驳需求预测方法——以北京草桥站为例

由于服务客群的多样化,复合型轨道交通车站的接驳需求、接驳设施配置及布局原则等均与普通轨道交通车站存在差异.通过分析不同服务客群在出行特征及交通接驳需求上的差异,提出复合型车站的接驳需求预测方法.以北京草桥站为例,在其区位、功能定位、现状及规划条件的基础上,通过对大兴机场线服务的航空客流、19号线及10号线服务的普通客流的出行特征及接驳需求的分析,得到多类型客流下车站分方式接驳需求量及设施规模.最后,结合出行特征及用地条件对车站交通接驳设施进行设置与布局.

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

一种基于物理核函数高斯过程回归的月径流预报模型及其应用

鉴于传统的单一径流预报模型很难描述径流未来变化规律,将自适应变分模态分解(AVMD)与基于组合物理核函数的高斯过程回归(GPR-CK)相结合,构建了AVMD-GPR-CK预报模型,该模型采用AVMD将实测径流分解为多个子序列,对子序列依据其自身特点分别建模,子序列预报结果叠加重构即为最终预报结果。模型应用于金沙江流域向家坝站未来1~12个月的径流预报的结果表明,所有预见期AVMD-GPR-CK模型的确定性系数均大于0.94,平均绝对百分比误差(M_(MAPE))在±17%以内,预见期在10个月以内时,M_(MAPE)在±10%以内;预报精度明显优于常见的BP、GRNN、RBF、RELM模型。

集成机器学习预测算法的短期负荷预测

为了提供与电力负荷相匹配的稳定高效的能源,减少电能因难以储存而造成的浪费,提出一种基于注意力机制、一维卷积神经网络和长短期记忆网络并行结合的负荷预测模型。首先,对山西省某市的负荷特征数据预处理;然后将数据并行输入到模型中进行训练,对模型优化进而获得更准确的短期预测能力;最后将所提模型与其他预测模型在不同的时间步长下进行预测对比。结果表明,所提方法在预测中具有更高的准确率和一定的普适性。

基于MCQRDDC的负荷概率预测模型

针对具有约束性的复合分位数回归网络(monotone composite quantile regression neural network,MCQRNN)无法较好地分析负荷数据之中的时序信息和内在规律的问题,本研究融合MCQRNN以及膨胀因果卷积网络(dilated causal convolutional networks,DCC),提出了一种新的分位数回归模型MCQRDCC(monotone composite quantile regression dilated causal convolutional networks),该模型将输入划分为分位点输入与非约束输入,使该模型的输出随分位点的增大而增大,以此解决分位数交叉的问题.同时,使用DCC的结构,使该模型充分地分析负荷数据之中的序列信息,使得预测结果更加符合真实负荷的变化趋势.此外,MCQRNN使用指数函数对约束权重矩阵和隐藏层权重进行转化,会影响反向传播时权重的调整,本研究使用ReLU函数代替指数函数可以解决这个问题,以此提高预测的精度.使用真实的负荷数据进行实验,实验结果表明,MCQRDCC能有效地提高预测精度,相较于MCQRNN,其平均Pinball损失和CWC分别下降2.11%和9.31%,AIS提升了10.51%.

产量构成法中措施产量劈分及预测的两种方法

为配合中国石油天然气股份有限公司已开发油田老井产量项目管理工作,给出两种从开发单元原油产量中劈分和预测某些批次油井(参照对象和项目对象)措施增油量的方法:在明确类比对象及其类比参数的求法的前提下,以前几年内整个开发单元产量数据和开发单元投产新井单井产量数据为劈分基础,经回归参照对象的历史参数值得出定量预测模型,进而用类比法预测项目对象在后几年的措施增油量。方法一的回归参数为产量构成法求出的参照对象在历年的措施增油量,从而直接预测项目对象在后几年的措施增油量;方法二则通过两个回归参数(产量构成法求出的参照对象历年的总产量和自然产量)间接预测项目对象在后几年的措施增油量。方法二具有理论依据,在处于递减阶段的开发单元的项目对象措施增油量预测中,可信度较大。

钢-混凝土组合梁的研究与展望

介绍国内外叠合式钢-混凝土组合梁应用及研究成果,分析这种组合梁存在的主要问题,重点探讨各种最新研究的型钢-混凝土组合梁的受力及变形特点,在分析的基础上,认为加强钢-混凝土间的锚固、纵向截面的抗剪承载力以及斜截面的抗剪承载力是叠合式钢-混凝土组合梁发展应解决的关键问题,提出的新型组合梁加强了钢与混凝土间的粘结,提高了结构的抗剪能力及抵抗变形的能力。

纳米材料制备技术及其研究进展

系统介绍了纳米材料体系中的纳米微粒、纳米薄膜和纳米复合材料的制备技术 ,对各种技术的特点进行了评述 。

非等时距GM(1,1)直接模型及其在材料试验数据处理中的应用

在原始数列等时距处理的基础上 ,通过用一次累减数列与原始数列构建微分模型 ,得到了非等时距GM( 1 ,1 )直接模型 ;并给出 2个具有不同饱和特征的材料试验数据处理实例 .通过这 2个实例说明了非等时距GM( 1 ,1 )直接模型适合处理呈上升或下降饱和变化趋势、对数据无非负性要求的任意数列 ,其预测值不需要还原计算 ,具有适用范围广、预测精度高和简单实用的特点 .该模型有效弥补了传统GM( 1 ,1 )模型在此类数据处理方面的不足 ,因此 ,具有较大的应用推广价值 .

平纹织物复合材料的弹性模量预测

利用细观力学的代表体积元 (RVE)法 ,预测了平纹织物复合材料的弹性模量。建立了表征 RVE形态的数学模型 ,分析了细观结构与宏观性能之间的关系 ,编制了从组分材料的弹性性能推测复合材料的弹性性能的预测程序。对理论预测进行了实验验证 ,理论值与实测值在一定误差范围内能较好吻合 。

综合指标方法在降水分级预报中的应用

利用 1995年 1月～ 1997年 12月国家气象中心的 T10 6分析场资料 ,采用因子组合、相关分析等手段 ,确定与降水关系密切 ,且有、无雨分类较明确的物理量因子 ,将样本内每个因子由小到大排列后分段 ,计算各段上的降水频率 ,根据各物理量对降水贡献的大小 ,确定各因子的权重系数 ,将每个样本中的所有因子所在段上对应的降水频率加权后累加 ,得到一个综合降水频率值 ,通过历史拟合 ,确定降水 TS评分最大的降水频率值为综合预报指标 ,分别建立小雨、中雨、大雨、暴雨的短期降水综合预报指标。利用该方法进行了降水分级预报 ,并对预报效果进行了分析对比。

煤矿复合动力灾害危险性实时预警平台研究与展望

为解决复合动力灾害危险性实时预警的难题,通过研究煤矿复合动力灾害发生机理和发生前兆的关联性,将复合动力灾害发生前兆分为共性和个性两类,并与监测参数类型相对应,提出了针对复合动力灾害危险性的临场预警、中期预警以及远期预警的关键监测参数。复合动力灾害危险性实时预警平台的构成主要包含监测硬件、分析软件及预警方法 3个部分,硬件主要指动力灾害监测系统及平台监控室硬件;软件主要指平台采集及分析软件;预警方法指标包括复合动力灾害危险性联合预警方法和单参数与多参数联合预警方法。在河南、山东等存在复合动力灾害的矿井进行了预警试验和基础研究,预警了矿井的复合动力灾害。同时,展望了复合动力灾害危险性实时预警平台的发展趋势。