灰色系统的交通流量短期预测

为了缓解交通拥堵问题,建立灰色模型对交通流量进行短期预测,从而提前知道道路的交通状况,最终起到交通诱导。     

一个区域气候-化学耦合模式的研制及初步应用

将区域气候模式RegCM 2和对流层大气化学模式TACM相耦合 ,建立了一个区域气候 -化学模式系统 (RegCCMS) ,主要用于对流层臭氧和硫酸盐气溶胶的时空分布、辐射强迫和气候效应研究 .利用 2 0 0 0年 7月NCEP再分析资料和RegCCMS模拟了东亚地区夏季对流层臭氧和硫酸盐气溶胶的空间分布 ,估计了相应的辐射强迫以及对近地面气温变化的影响 .研究表明我国夏季对流层臭氧和硫酸盐的月均最大地面浓度分别为 1 2 5× 1 0 - 9和 8μg/m3.对流层臭氧的平均辐射强迫为 0 .3 9W /m2 ,硫酸盐气溶胶的平均直接辐射强迫为 - 0 .92W /m2 ,两者皆在西南、华南和华中地区出现极值中心 .硫酸盐气溶胶的平均间接辐射强迫为 - 0 .2W /m2 ,华东和华南沿海地区相对较强 .在局部地区 ,对流层臭氧和硫酸盐气溶胶的辐射强迫高于IPCC( 2 0 0 1 )报导值 .总体而言 ,对流层臭氧可能导致的增暖效应明显弱于硫酸盐气溶胶的冷却效应 ,因此 ,净辐射强迫为负值 ,两者的综合效应造成近地面平均气温下降 - 0 .2℃ .西南地区降温较为明显 ,最大幅度达到 - 0 .5 7℃ .     

基于人工神经网络的实时短期负荷预测系统

应用人工神经网络设计短期电力负荷预测系统,利用广州电网的负荷数据进行仿真,分别作出提前１小时和未来２４小时的整点负荷预测,获得了比较满意的预测精度,显示出人工神经网络应用于短期电力负荷预测的良好前景。由于建立小时模型,改进了训练样本集的选取办法及采用高效率的ＬＭ训练算法,使ＡＮＮ的训练速度大大加快,形成可以实时训练和预测的ＡＮＮ－ＳＴＬＦ系统。     

区域气候模式的发展及研究进展

进入20世纪90年代以来,区域气候模拟研究的进展更加明显。文中将着重分析全球气候模式对区域气候模拟的可靠性与不确定性;国内外对区域气候的模拟研究进展。     

基于复杂系统的短期负荷预测研究及系统设计

运用复杂系统理论分析电力系统.采用聚类方法对用电区域进行子系统划分,并通过改进的神经元网络算法和增加天气因素的预测方法进行短期负荷预测.通过算例和电力系统应用,证实了该算法的可行性,较显著地提高了负荷预测的准确率.     

一种改进的相空间近邻等距模式及其在短期气候预测中的试验

现有研究表明,气候要素系统可能存在混沌现象.通过应用混沌理论中的重建相空间技术,可在相空间中揭示出传统方法无法揭示的复杂气候动力特征,为气候预测研究开创一条新途径.现有相空间理论的研究多着重于对混沌系统的识别和应用混沌相空间理论建立不同的模式进行提前预测,这些预测多为对月时间尺度的;对于如何提高预报准确率和延长预报时效,从而实现对多个月份、季、年等更长时间尺度提前预报的研究还不多见.基于混沌理论的相空间近邻等距模式,对该理论中的提前预报时间T、延迟时间τ和相空间维数d等进行讨论.提出,实际应用中根据提前预报时间T来建立新的时间序列,满足T=τ=sδt,消除了相空间时滞τ的变化对提前预报时间尺度T的影响.应用改进前、后的预报模式,以不同相空间维数d对气温和降水气候要素做提前T=1~15个月的预报试验和检验.结果发现,改进后的模式提高了预报准确率,延长了预报时效.     

电力系统短期负荷预测方法综述

短期负荷预测是电力系统安全经济运行的基础.介绍了电力系统短期负荷预测基本方法原理与特点,说明了各种方法的优缺点以及应用的局限性.     

短期气候预测的预报集成方法试验研究

依据武汉市 ６ 月、７ 月降水距平百分率 ３ 种预测模型的１１ 年独立样本试验资料,进行了预报集成方法的试验研究．通过 ４ 种方案共 ６ 种集成方法的试验比较得出：集成预报模式比原预报模式总体误差得到改善,采用不同的集成方法,同一原始模式的权重系数可以不同,并且只有提供好的原预报模式才能得到好的集成模式     

电力负荷短期预测与调度自动化系统的集成

基于广州电网负荷短期预测系统的开发，讨论了在调度自动化系统中实现短期负荷预测功能的集成要点，同时就软件层次、数据交换、进程管理以及界在处理等技术给出分析结论。     

Advancement and prospect of short-term numerical climate prediction

The defects of present methods of short-term numerical climate prediction are discussed in this paper, and four challenging problems are put forward. Considering our under developed computer conditions, we should innovate in the approcuch of numerical climate prediction on the basis of our own achievements and experiences in the field of short-term numerical climate prediction. It is possibly an effective way to settle the present defects of short-term numerical climate prediction.     

Predictor-based error correction method in short-term climate prediction

In terms of the basic idea of combining dynamical and statistical methods in short-term climate prediction, a new prediction method of predictor-based error correction (PREC) is put forward in order to effectively use statistical experiences in dynamical prediction. Analyses show that the PREC can reasonably utilize the significant correlations between predictors and model prediction errors and correct prediction errors by establishing statistical prediction model. Besides, the PREC is further applied to the cross-validation experiments of dynamical seasonal prediction on the operational atmosphere-ocean coupled general circulation model of China Meteorological Administration/ National Climate Center by selecting the sea surface temperature index in Niño3 region as the physical predictor that represents the prevailing ENSO-cycle mode of interannual variability in climate system. It is shown from the prediction results of summer mean circulation and total precipitation that the PREC can improve predictive skills to some extent. Thus the PREC provides a new approach for improving short-term climate prediction.     

Predictor-based error correction method in short-term climate prediction

In terms of the basic idea of combining dynamical and statistical methods in short-term climate prediction, a new prediction method of predictor-based error correction （PREC） is put forward in order to effectively use statistical experiences in dynamical prediction. Analyses show that the PREC can reasonably utilize the significant correlations between predictors and model prediction errors and correct prediction errors by establishing statistical prediction model. Besides, the PREC is further applied to the cross-validation experiments of dynamical seasonal prediction on the operational atmosphere-ocean coupled general circulation model of China Meteorological Administration/National Climate Center by selecting the sea surface temperature index in Nifio3 region as the physical predictor that represents the prevailing ENSO-cycle mode of interannual variability in climate system. It is shown from the prediction results of summer mean circulation and total precipitation that the PREC can improve predictive skills to some extent. Thus the PREC provides a new approach for improving short-term climate prediction.     

Introduction and systematic assessment for IAP numerical annual climate prediction system

The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. Systematic assessment shows that this annual prediction system has higher predictability for summer climate in tropic than in extra-tropic area, and higher predictabilities over ocean than over land for the fields of precipitation, sea level pressure and surface air temperature; for 500 hPa geopotential height field, the predictability assuming a zonal distribution decreases from tropic to middle-high latitudes, and in China it is the highest among those of all fields. Correlation analysis shows that the prediction ability of IAP annual prediction system to summer temperature is higher than that to precipitation, and the prediction skill can be remarkably improved by the correction system. Furthermore, the comparison between annual and extraseasonal hindcasts indicates that precipitation hindcasted extraseasonally is better than that done annually, and the major discrepancy exists in middle-high latitudes.     

The coupling procedure of air-sea freshwater exchange in climate system models

A coupling procedure of air-sea freshwater exchange in climate system models is reported in this note. The first stage of the procedure is to force OGCM to equilibrium under strong restoring surface condition on salinity, then increase the relaxing coefficient and get another steady state. The second stage is to switch the forcing on salinity from the weak restoring condition to the flux condition, and then finish a long-term spinning-up integration. After finishing these OGCM spinning-up stages, the last stage is to couple the OGCM with an active atmosphere, i.e. AGCM. Verification with the Global-Ocean-Atmosphere-Land-System model developed at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) shows that the preferred procedure is successful in including the air-sea freshwater exchange process.     

新的集成预报及其在短期气候预测中的应用

分析了传统的基于加权的集成预报等方法及其在气象预测应用中存在的问题,在此基础上提出了一种新的基于数据挖掘的集成预报方法,并选用BP人工神经网络建立集成预报分类器来对各种子预报方法的预报结果进行集成和综合;该方法可以根据不同预报对象的特性,对集成预报权值进行动态改变,克服了传统的集成预报方法中权值一旦确定就不能改变的不足,也克服了现有的集成预报不能得到最优结果的不足。通过对2001～2007年重庆市城口县1月的降水和平均气温以及重庆市的春旱指数进行预报,实验结果显示,集成预报结果的可靠性和准确性不但高于集成之前的各种子预报方法,而且高于传统的其它集成预报方法,验证了方法的有效性。     

Kalman滤波理论在短时交通预测上的应用

介绍了基于Kalman滤波理论的短时段交通量预测模型,并利用京沪高公路和南京禄口机场高速公路所采集的数据进行了仿真预测.预测结果表明Kalman滤波预测方法对各种状态交通流有较好的适应性,具有较高的预测精度.     

企业财务困境短期与中长期预测指标的比较实证研究

首先分析企业财务困境的发生过程,然后提出系统化的预测指标选择方法,并根据该方法分别建立企业财务困境的短期和中长期预测指标体系,以研究反映企业短期与中长期财务状态的关键指标及其差别.最后使用建立的预测指标体系进行提前期为1年至5年的财务困境预测,通过实证分析说明其合理性和有效性.实证结果还表明,不同提前期下财务困境预测需要不同的预测指标.     

Coupling methods of global climate models and regional climate models

The future climate dynamical downscaling method is that output of general circulation models (GCMs) is employed to provide initial conditions,lateral boundary conditions,sea surface temperatures,and initial land surface conditions to regional climate models (RCMs).There are two methods of downscaling:offline coupling and online coupling.The two kinds of coupling methods are described in detail by coupling the Weather Research and Forecasting model (WRF) with the Institute of Atmospheric Physics of Chinese Academy of Sciences Atmospheric General Circulation Model Version 4.0 (IAP AGCM4.0) in the study.And the extreme precipitation event over Beijing on July 21 2012 is simulated by using the two coupling methods.Results show that online coupling method is of great value in improving the model simulation.Furthermore,the data exchange frequency of online coupling has some effect on simulation result.     

用于短期风功率预测的历史数据深度迁移模型

随着全球化石燃料短缺日益严重,可再生能源的开发与利用愈发得到重视。风能是被广泛使用的清洁能源之一,在生产工作中,风力发电作为风能的主要利用形式,需要对其功率进行预测。依托风场日常记录的历史数据,传统学习模型可对风功率进行短期预测,但往往仅使用自己域内的历史数据作为分析对象,该类算法导致结果片面,局限性大,不能有效使用类数据中的隐含联系,抑制原始数据缺失或异常值引起的模型性能下降问题。笔者设计一种基于历史数据深度迁移的短期风功率预测模型。首先,使用带降噪处理的自动编码机构建深度神经网络模型。其次,应用深度迁移方法共享隐藏层,挖掘特征之间的隐含联系。最后,从具有相似特征和地理位置的风场数据中迁移重要知识,提高模型准确率和可靠性。实验结果表明,研究方法较之未使用迁移的方法更充分利用现有数据,预测准确率显著提高。