地区电力需求预测研究

本文在分析影响预测准确原因的基础上．指出预测工作必须定量与定性相结合．针对地区电力消费需求的特点，提出了适宜于定性分析的四个预测模型．运用上述模型，实际预测结果令人满意。     

考虑气象因素的月度电力需求预测方法

气象因素是电力需求变化的重要影响因素之一。为了研究气候变化对月度电力需求的影响,建立了包含气象条件、时间趋势和春节移动假日等变量的多元回归方程,以苏州市月度电力需求预测为例,重点分析了气象因素和春节移动假日对月度电力需求的影响。研究结果发现,使用月平均气温计算出的月制冷度(Cooling Degree of Month,CDM)和月采暖度(Heating Degree of Month,HDM)能较好的刻画气温变化对月度电力需求的影响;考虑了春节假日效应后,能更好的提升回归方程的预测能力;非“疫情”期间和“疫情”期间模拟预测结果均达到较好的精度,显示出模型具有良好的预测稳健性。     

基于蒙特卡罗方法的中国中长期煤炭需求预测

为避免传统预测方法的参数取值主观性问题,采用参数随机产生的蒙特卡罗方法预测中国中长期煤炭需求。首先分析了经济增长、能源结构和产业结构三个主要煤炭需求影响因素,并基于1980~2015年间各影响因素及煤炭消费的历史数据和最小二乘法的多元线性回归拟合煤炭需求方程。在此基础上,构建各影响因素的概率分布,采用蒙特卡罗方法模拟1981~2015年的煤炭需求,发现仿真结果可以较好拟合现实,可作为仿真预测的有效工具。结合经济新常态和能源结构调整的现状,控制参数取值范围进行蒙特卡罗仿真预测,结果显示,2016~2025年的煤炭需求呈先上升后下降趋势,并于2020年达到需求的峰值40.25亿吨,这些结果对于煤炭产业的科学决策有重要的作用。     

基于组合预测模型的物流园区物流需求预测——以重庆空港物流园为例

物流需求预测是物流园区整体规划的重要前提,准确的物流需求预测可以大大提高物流园区规划的科学性.首先建立了趋势曲线预测模型、回归预测模型及灰色预测模型的物流需求单项预测模型,鉴于单项预测模型的局限性,然后以Shapley值为权重确定方法,建立了组合预测模型,并以重庆空港物流园为例进行应用,最后得出了2015年和2020年重庆空港物流园物流需求的预测值.研究表明,组合预测比单项预测具有更高的精度和稳定性,方法在物流园区物流需求预测中具有推广应用价值.     

我国未来能源投资需求预测及特点分析

持续的能源投资是经济持续发展的必要保障.鉴于我国能源投资的复杂性,基于系统动力学和成本预测方法,预测了我国2006-2050年期间的能源投资需求,分析了各能源品种的投资需求特点及不确定性因素.结果发现,我国能源投资将保持持续增长态势,但能源投资占GDP的比重不断下挫.而且,电力投资将一直占据我国能源投资的主导地位.     

用概率统计方法预测洪水水位

河流的水位变化从局部时间看是偶然的，但从长远看，这些偶然的数据常常是必然的结果。在自然条件下取得的水位数据，从本质上看都是随机的，正因为其随机性，统计方法尤为重要。本文试用统计方法分析预测河水的最高洪水水位，这对建筑桥梁、修建堤坝具有一定的参考价值。     

加权拟合直线方程法在旅游需求预测中的应用

应用加权拟合直线方程法对哈尔滨市旅游需求进行预测,并通过实际旅游人数进行验证、探讨此方法的有效性.     

基于模糊统计的公司违约预测

针对KMV违约预测模糊中固定违约点的缺陷，将违约点模糊化，以模糊事件表示违约，从而修改确定公司股权价值的期权公式，进一步得到违约概率预测，重点讨论计算违约模糊事件的基本思路，详细给出模糊统计方法，通过案例分析说明本文提出的模糊方法的可行性与长处。     

应用统计分辨原理预报害虫种群动态

本文应用基于Fisher准则下统计分辨原理，对山东省曲阜市1982～1994年棉花苗期棉蚜的系统观测资料进行了数量分析，建立了统计分辨数学模型：y＝－0．2359x1－0.0049x2，经对历史资料的回报验证，其历史拟合率为92．31％．将1995年观测数据作为独立样本进行试报，其预报结果与实际一致．     

基于灰色系统模型的山西省物流需求预测分析

选取山西省2007-2013年的货运周转量、全省生产总值、人均生产总值、进出口总额、公路里程、铁路营业里程、公路载货汽车拥有量等10个影响因素,利用灰色关联分析对影响山西省物流发展的这些影响因素进行分析,结果显示人均生产总值对山西省物流发展的影响力度最大,选取货运周转量为特征因素,人均生产总值为其相关因素,利用灰色预测模型GM(1,2)模型,对山西省未来3年的物流需求进行预测.预测结果显示:模型具有较高的预测精度,对山西省物流的规划和制2发展战略起到参考作用.     

k-U统计量的指数收敛速度

研究了k-U统计量的收敛速度,在一组适当的正则条件下,获得了k-U统计量的指数收敛速度,推广了U-统计量的指数收敛速度的相应结果.     

广义Vandermonde行列式的计算公式

给出两类广义Vandermonde行列式的计算公式以及各类广义Vandermonde行列式的计算方法.     

On asymptotic distributions of exceedance statistics

In this study, two independent samples X₁,X₂,…,X_n and Y₁,Y₂,…,Y_m with respective distribution functions F and Q are considered. The joint asymptotic distributions of exceedance statistics defined as the number of Y observations falling into a random interval of order statistics constructed from the X sample is investigated. The results can be used in the context of a two-sample problem.     

The uniform convergence and precise asymptotics of generalized stochastic order statistics

Let X(i,n,m,k), i=1,…,n, be generalized order statistics based on F. For fixed r∈N, and a suitable counting process N(t), t>0, we mainly discuss the precise asymptotic of the generalized stochastic order statistics X(N(n)−r+1,N(n),m,k). It not only makes the results of Yan, Wang and Cheng [J.G. Yan, Y.B. Wang, F.Y. Cheng, Precise asymptotics for order statistics of a non-random sample and a random sample, J. Systems Sci. Math. Sci. 26 (2) (2006) 237-244] as the special case of our result, and presents many groups of weighted functions and boundary functions, but also permits a unified approach to several models of ordered random variables.     

Convergence Acceleration of Direct Trajectory Optimization Using Novel Hessian Calculation Methods

Sparse sequential quadratic programming (SQP) has offered fast and robust convergence of trajectory optimization based on direct collocation. However, the conventional approach of calculating the Hessian of the Lagrangian is sometimes inefficient in view of the computational time. Therefore, this paper proposes two novel Hessian calculation methods that exploit the doubly-bordered block diagonal structure of the Hessian. Through applications to the constrained brachistochrone problem and the space shuttle reentry problem, the proposed methods were demonstrated to show faster convergence speeds as compared with the conventional methods. This work was supported by a Grant-In-Aid from the Japan Society for the Promotion of Science.     

Scan statistics analysis of forest fire clusters

Spatio-temporal clusters in 1997–2003 fire sequences of Tuscany region (central Italy) have been identified and analysed by using the scan statistic, a method which was devised to evidence clusters in epidemiology. Results showed that the method is reliable to find clusters of events and to evaluate their significance via Monte Carlo replication. The evaluation of the presence of spatial and temporal patterns in fire occurrence and their significance could have a great impact in forthcoming studies on fire occurrences prediction.     

Ordering distributions by scaled order statistics

Motivated by applications in reliability theory, we define a preordering (X ₁, ...,X _n) (Y ₁ ...,Y _n) of nonnegative random vectors by requiring thek-th order statistic ofa ₁ X ₁,..., a _n X _n to be stochastically smaller than thek-th order statistic ofa ₁ Y ₁, ...,a _n Y _n for all choices ofa _i >0,i=1, 2, ...,n. We identify a class of functionsM _{k, n} such that if and only ifE(X)E(Y) for allM _k,n. Some preservation results related to the ordering are obtained. Some applications of the results in reliability theory are given.Supported by the Air Force Office of Scientific Research, U.S.A.F., under Grant AFOSR-84-0205.     

Stability of invariant linearly sufficient statistics in the general Gauss-Markov model

Necessary and sufficient conditions are derived for the inclusions and to be fulfilled where are some classes of invariant linearly sufficient statistics (Oktaba, Kornacki, Wawrzosek (1988)) corresponding to the Gauss-Markov models , respectively.     

Maximum variance of order statistics

Yang (1982,Bull. Inst. Math. Acad. Sinica,10(2), 197–204) proved that the variance of the sample median cannot exceed the population variance. In this paper, the upper bound for the variance of order statistics is derived, and it is shown that this is attained by Bernoulli variates only. The proof is based on Hoeffding's identity for the covariance.