基于ARIMA-GM组合模型的邮电业务总量预测

对传统预测具有波动性及季节性双重趋势时间序列的模型—ARIMA乘积季节模型进行了改进,先用ARIMA乘积季节模型对邮电业务总量历史数据进行识别和拟合,然后用GM(1,1)模型对其带阀值的残差序列进行修正,最后结合二者得到ARIMA-GM这一组合预测模型.利用此模型对09年上半年中国邮电业务总量进行了预测,结果表明,组合预测方法比单项ARIMA乘积季节模型预测具有更高的精度.     

基于时间序列方法的全国铁路旅客周转量的分析与预测

应用两种时间序列分析的方法对全国铁路旅客周转量的月度数据进行分析.运用X-11方法和季节ARIMA模型进行分析并分别对未来5个月的周转量做了预测,结果表明季节ARIMA模型优于X-11方法.通过对全国铁路旅客周转量的定量分析,为铁路部门在计算运输成本,劳动生产率,旅客平均行程等方面提供有效的依据.     

季节-谐波油价预测模型研究

油价预测是能源市场研究的一个重要领域.本文基于季节调整技术和周期性分析技术,提出了一个新的预测模型-季节-谐波模型,其思路是分解-组合.通过对五种油品(WTI原油、Brent原油、无铅汽油、柴油和取暖油)价格的实证分析,与其它五种方法(ARIMA模型、指数平滑、Winters方法、EGARCH模型和逐步自回归)相比,季节-谐波模型取得了最好的预测效果.     

中国入境旅游客源量的模型对比分析及预测

根据2000年1月至2009年12月中国入境旅游客源量的月度统计数据,建立了灰色预测GM(1,1)模型和ARIMA乘积季节模型.借助于MATLAB及SPSS软件,对2009年1月至2009年6月中国入境旅游客源量进行分析预测,并将两种模型的预测效果进行比较,从而探索出比较合适的短期预测方法,预测方法和结果对旅游规划具有一定的参考价值.     

基于时间序列模型的全国30家电台收视率分析

以湖南电视台和北京卫视为代表,对全国30家电视台卫星频道2008全年日收视率进行时间序列分析,建立了四种拟合模型.其中,大部分电台收视率具有长期趋势或长期趋势和季节效应的综合影响,分别建立ARIMA模型和乘积季节模型,并进行预测,其结果表明模型拟合效果较好.     

基于修正的时间序列模型对冲击负荷的预测

电力负荷预测的实质是对电力市场需求的预测,是利用以往的历史数据资料找出电力负荷的变化规律,进而预测负荷在未来时期的变化趋势.由于经济、气候以及工业生产等诸多因素的约束和限制,电力负荷预测精度很难提高.一个好的实用的电力负荷预测模型则要求既能充分利用负荷的历史数据,又能灵活方便地综合考虑其他多种相关因素的影响.提出了回归与自回归模型相结合的时间序列混合回归预测模型,它的待估参数由BP神经网络进行修正,经实例验证,预测效果良好.     

南通地区月降水量时间序列分析

根据南通地区1989年-2005年月降水量数据,在统计检验其平稳性、纯随机性的基础上,结合谱分析,建立该地区具有季节效应的疏系数ARIMA月降水量时间序列模型,对模型作了拟合预测检验.研究表明,多个模型的联合使用比单一模型更利于准确拟合预测.     

ARIMA模型在京津冀区域手足口病发病趋势预测中的应用

本文以时间序列理论为基础,建立了乘积季节ARIMA预测模型。由于手足口病具有明显的季节性,且其发病潜伏期一般为3-7天,因此本文以周为时间尺度,即定义季节周期为53周,探讨了季节性乘积模型在手足口病发病趋势预测中的应用,并通过AIC和BIC信息准则确定最优模型。通过2009-2013年京津冀区域手足口病监测数据实证了季节性ARIMA(p,d,q)×(P,D,Q)53模型的预测效果。结果显示,该模型对手足口病高发时间的发病数预测效果较好,预测发病趋势与实际发病趋势相一致。     

GM（1，1）周期修正模型及在电力负荷预测中的应用

模型GM(1,1)是电力系统负荷预测的一种有效的方法,但利用GM(1,1)模型难以反映序列随机的季节性周期波动变化.本文阐述的周期修正模型,可以很好地解决这一问题.实例表明,此模型简单有效,对于季节性预测问题有很强的实用性和较高的预测精度.     

基于ARIMA和BP神经网络组合模型的能源消费预测

在单一的ARIMA模型预测的基础上,提出了一种基于ARIMA模型和BP神经网络组合模型,并以广西区1981至2016年的能源消费数据为例,结果表明组合模型的预测精度要优于单一的模型,故利用组合模型对广西区未来能源消费的预测更加有效.     

考虑风电出力及负荷预测误差的安全经济运行模型

充分考虑了风电并网和负荷预测不确定性,引入净负荷的概念,实现对风电和负荷预测误差发生概率的综合考虑。通过场景概率的研究,以包括确定性成本和随机性成本在内的综合调度成本最小化为目标函数,构建安全经济运行模型。算例分析表明,本文构建的随机安全运行计划模型相比于传统的确定性模型能够更有效地降低系统运行成本,结果同时显示,根据系统容量和负荷需求合理配置风电装机容量,是减少弃风量、提高供电可靠性的重要手段。     

Robust placement and tuning of UPFC via a new multiobjective scheme‐based fuzzy theory

This article addresses a new modified honey bee mating optimization namely multiobjective honey bee mating optimization (MOIHBMO) based fuzzy multiobjective methodology for optimal locating and parameter setting of unified power flow controller (UPFC) in a power system for a long‐term period. One of the profits obtained by UPFC placement in a transmission network is the reduction in total generation cost due to its ability to change the power flow pattern in the network. Considering this potential, UPFC can be also used to remove or at least mitigate the congestion in transmission networks. The other issue in a power system is voltage violation which could even render the optimal power flow problem infeasible to be solved. Voltage violation could be also mitigated by proper application of UPFC in a transmission system. These objectives are considered simultaneously in a unified objective function for the proposed optimization algorithm. At first, these objectives are fuzzified and designed to be comparable against each other and then they are integrated and introduced to a MOIHBMO method to find the solution which maximizes the value of integrated objective function in a 3‐year planning horizon, considering the load growth. A power injection model is adopted for UPFC. Unlike, the most previous works in this field the parameters of UPFC are set for each load level to avoid inconvenient rejection of more optimal solutions. IEEE reliability test system is used as an illustrative example to show the effectiveness of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 126–137, 2015     

Sparsity-promoting distributed charging control for plug-in electric vehicles over distribution networks

Uncoordinated charging of plug-in electric vehicles brings a new challenge on the operation of a power system as it causes power flow fluctuations and even unacceptable load peaks. To ensure the stability of power network, plug-in charging needs to be scheduled against the base load properly. In this paper, we propose a sparsity-promoting charging control model to address this issue. In the model, the satisfaction of customers is improved through sparsity-promoting charging where the numbers of charging time slots are optimized. Dynamic feeder overload constraints are imposed in the model to avoid any unacceptable load peaks, and thus ensure the network stability. Then, a distributed solution strategy is developed to solve the problem based on the alternating direction method of multipliers (ADMM) since most of power networks are managed typically in a distributed manner. During solving process, Lagrangian duality is used to transform the original problem into an equivalent dual problem, which can be decomposed into a set of homogeneous small-scaled sub-problems. Particularly, each sub-problem either has a closed-form solution or can be solved locally by an accelerated dual gradient method. The global convergence of the proposed algorithm is also established. Finally, numerical simulations are presented to illustrate our proposed method. In contrast to traditional charging models, our sparsity-promoting charging model not only ensures the stability of power network, but also improves the satisfaction of customers.     

Optimal economic load dispatch based on wind energy and risk constrains through an intelligent algorithm

This article focus on optimal economic load dispatch based on an intelligent method of shark smell optimization (SSO). In this problem, the risk constrains has been considered which has root in uncertainity and unpredictable behavior of wind power. Regarding to increasing of this clean energy in power systems and un‐dispatchable behavior of wind power, its conditional value at risk index considered in this article which consists of loss from load and "spilling" wind energy connected with unpredictable imbalances among generation and load. This problem has been considered as an optimization problem based on SSO that evaluate the balance between cost and risk. This algorithm is based on distinct shark smell abilities for localizing the prey. In sharks' movement, the concentration of the odor is an important factor to guide the shark to the prey. In other words, the shark moves in the way with higher odor concentration. This characteristic is used in the proposed SSO algorithm to find the solution of an optimization problem. Effectiveness of the proposed method has been applied over 30‐bus power system in comparison with other techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 494–506, 2016     

Optimal generation scheduling for constrained multi-area hydrothermal power systems with cascaded reservoirs

The optimization problem in this paper is targeted at large-scale hydrothermal power systems. The thermal part of the system is a multi-area power pool with tie-line constraints, and the hydro part is a set of cascaded hydrostations. The objective is to minimize the operation cost of the thermal subsystem. This is an integer nonlinear optimization process with a large number of variables and constraints. In order to obtain the optimal solution in a reasonable time, we decompose the problem into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. For the hydro subsystem, network flow concepts are adopted to coordinate water usage over the entire study time span, and the reduced gradient method is used to overcome the linear characteristic of the network flow method in order to obtain the optimal solution. In this study, load forecasting errors and forced outages of generating units are incorporated in system reliability requirements. Three case studies for the proposed method are presented.     

Mathematical Modelling of Diesel-Electric Propulsion Systems for Marine Vessels

This article presents a mathematical model of a complete diesel-electric propulsion system, including components as diesel generators, distribution network, variable speed thruster-drives, and conventional motor loads. The model is split into two parts: One power generating part where the load is specified with an aggregated active and reactive power load demand. Secondly, a power consumption part where the effects of the different load types as thruster drives, motors and other loads are modelled. The model is written in a state-space form suitable for the purpose of simulation and control design. PID-controllers represent speed governors and automatic voltage regulators.     

Long-term optimal operation of series parallel reservoirs for critical period with specified monthly generation and average monthly storage

We present in this paper an efficient approach for solving the problem of planning the long-term (multiyear) operation of a multireservoir hydroelectric power system for the critical period with a monthly variable load. This load is equal to a certain percentage of the total generation at the end of the year, subject to satisfying a number of constraints on the hydrosystem, using the minimum norm formulation.The proposed method is efficient in computing time and in calculating the total expected benefits from the system during the critical period. Numerical results are reported for a real system in operation consisting of two rivers. Each river has two series reservoirs.This work was supported by the Natural Science and Engineering Research Council of Canada, Grant No. A4146.     

A Problem in Reliability Optimization

We consider the system efficiency measure as the average asymptotic gain earned by the system for one time unit. Such defined efficiency is a continuous function of the load that the system is to support. Each increment of the load causes an increment of the gain. On the other hand, it also causes a loss of system reliability, giving in this way a loss. This loss follows from the fact that a less reliable system is in the off state more frequently than in the lower-load case. The goal is to find the load that maximizes the efficiency.The lifetime distribution of the system is defined as exponential. As models of the relationship between the load and the gain as well as the relationship between the load and the failure rate, power and exponential functions have been taken. The analytical conditions for the existence of maximum positive gain and the analysis of the behaviour of the gain as the load increases are presented.     

Passivity-based adaptive control of chaotic oscillations in power system

Recent investigations have shown that with stronger periodic load perturbation power system is experiencing complex chaotic oscillations which threaten the secure and stable operation of power system, even induce the interconnected power system collapse. To control these undesirable chaotic oscillations, a passivity-based adaptive control law is presented in this paper, which transforms the power system into an equivalent passive system. It is proved that the equivalent system can be asymptotically stabilized at different equilibrium points without influence of undeterministic parameters. Simulation results show the proposed control law is very effective and robust against both the uncertainty in system parameters and external noise interference. The research of this paper may help to maintain the power system’s security operation.     

Clustering approach to model order reduction of power networks with distributed controllers

This paper considers the network structure preserving model reduction of power networks with distributed controllers. The studied system and controller are modeled as second-order and first-order ordinary differential equations, which are coupled to a closed-loop model for analyzing the dissimilarities of the power units. By transfer functions, we characterize the behavior of each node (generator or load) in the power network and define a novel notion of dissimilarity between two nodes by the \(\mathcal {H}_{2}\)-norm of the transfer function deviation. Then, the reduction methodology is developed based on separately clustering the generators and loads according to their behavior dissimilarities. The characteristic matrix of the resulting clustering is adopted for the Galerkin projection to derive explicit reduced-order power models and controllers. Finally, we illustrate the proposed method by the IEEE 30-bus system example.