Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case

The overall electricity consumption, treated as a primary guideline for electricity system planning, is a major measurement to indicate the degree of a nation's development. The electricity consumption forecast is especially important with regard to policy making in developing countries (Asian countries in this work). However, since the economic growth rates in these countries are usually high and unstable, it is difficult to obtain accurate predictions using long-term data, and thus forecasting with limited (short-term) data is more effective and of considerable interest. Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. The forecasting performance of AGM(1,1), based on grey theory, has been confirmed using the Asia-Pacific economic cooperation energy database, and the results, compared with those obtained from back propagation neural networks (BPN) and support vector regression (SVR), show that the proposed approach can effectively deal with the problem of forecasting electricity consumption when the sample size is limited.     

Accuracy in the long run—Where are we now

Long-term forecasting must be viewed as informative speculation about the future. It should be credited with relatively little scientific authenticity. At the present stage of theory and data, no magic methods can be expected to overcome the problem of satisfactory forecasting. Improvement, however, is certainly not just a question of putting more and more variables and more and more numbers into a computer. Methods which indicate how to cushion against uncertainty and methods which bring a greater awareness of options for the future are essential if we are to gain greater control over events. The hazy images of the long-term future which are generated by scenario and other forms of analysis form the guidelines within which short and especially medium-term choices must be made. But in the end we must recognise the inherent limitations of forecasting and forecasting methods and think of forecasting not so much as a method of prediction but as a contribution to tackling the future in a more integrated sense.     

跨国公司对中外合资企业控制关键点的实证研究

有效控制合资企业是跨国公司实现全球战略一体化的要求。但跨国界控制的高成本,东道国政策限制等约束使跨国公司不能实施对合资企业的全面控制。而对跨国合资企业关键点控制的研究多是基于案例分析的结果,既无理论支撑,又无大样本检验,因此缺乏普遍性和说服力。本文在合资企业知识控制理论分析的基础上,提炼出跨国合资企业知识控制的关键点,并通过426家制造业中外合资企业的实证研究证明,跨国公司控制合资企业的关键点是重点控制和计划控制中的六个方面。     

待遇预定制养老基金资产组合与缴费计划最优决策——基于随机波动率Heston模型及Legendre对偶变换法

待遇预定制养老金制度在中国应用非常广泛,缴费制定和资产配置是此类养老金管理的两大核心问题。由此,面对随机波动的现实市场,文章针对待遇预定制养老基金的资产组合管理问题,应用最优控制理论,选用对数效用函数,建立Heston随机波动率模型;在难以求解随机微分Bellman方程的情况下,应用Legendre变换,将原来问题转化为对偶问题,从而求得原问题的解析解。在理论上,进一步丰富了资产组合问题的随机最优控制模型的构建和随机微分方程的求解理论。在实践上,确定了养老金管理风险资产配置比例和缴费水平,给出了最优决策与总资产、发放待遇、净资产与风险溢价之间的数量关系,从而实现养老基金管理的最优资产配置和最低缴费水平的效用目标。     

中国进出口贸易市场的混沌特性分析

对于经济管理系统混沌的检验,主要体现在证券、期货和外汇市场,由于其他经济领域序列数据的匮乏,传统混沌分析方法的应用受到了限制。本文采用最新的基于小数据样本的临近返回检验,研究中国进出口贸易1989年1月到2003年5月的月度数据序列,发现了混沌的拓扑特征;进一步应用小数据量方法计算最大Lyapunov指数,数值结果同样证实中国进出口贸易市场存在低维混沌,从而佐证了国际贸易市场混沌的存在;实证结果为国际贸易系统模型的建立和预测提供了新的理论依据。     

风险厌恶零售商考虑信息预测成本的协调机制

在CVaR风险度量准则下,将信息预测成本作为运营成本之一,建立了风险厌恶的零售商的需求信息预测投入和订货量的联合决策随机模型,研究了风险态度对零售商最优决策的影响.通过分析得出在分散决策下,随着零售商风险厌恶程度的增加,零售商提高信息预测水平,以减小需求不确定风险.同时减少订货量,降低订货过量的风险.证明了在一定条件下,传统的收益共享契约能实现供应链协同,但适用范围较小.由信息预测成本分担与收益共享组成的联合契约可扩大供应链协同的范围,消除风险态度和双重边际对零售商决策行为的影响,使得零售商的需求预测水平和订货量同时达到系统风险中立环境下的最优水平.     

农产品销量预测的支持向量机方法

运用支持向量机(Support Vector Machine,SVM)智能预测方法对农产品的消费市场需求进行动态预测。为提高农产品销量预测精度,充分考虑了农产品供需随天气变化、气候条件、节假日等因素的影响而动态变化的情况,将这些影响因素纳入农产品销量预测中,运用模糊理论进行模糊化处理;在此基础上提出以支持向量机方法为主、多方法融合为辅的智能预测系统,对农产品销量进行动态预测。实际算例验证了这一智能预测系统的精确性。     

Calendar of events

Abstract. Counterbalancing is a new method of forecasting that reduces the systematic component of forecasting error. A graphical interpretation of the method is presented. This intuitive approach reveals the need for variable as opposed to fixed equal weights. The method is expanded to counterbalancing with variable weights, resulting in further reductions in forecasting error. Important applications include (1) power system hourly load forecasting for economic dispatch, (2) information feed forward in continuous process control, and (3) forecasting for scheduling, just in time manufacturing, sales, and distribution requirements planning in global logistics.     

具有多元马氏需求的多产品多阶段库存优化模型

本文研究一类新的多产品库存控制策略,即具有多元马氏需求特征的多产品多阶段的订货点订货量(Q, R, SS)策略,该策略考虑市场需求在不同产品之间具有多元马氏转移特征,并考虑缺货因素设置安全库存。论文首先建立了多产品多阶段的多元马氏需求预测模型,并通过该模型确定了各种产品需求之间的关系。同时,在该模型的理论基础上,提出了多产品多阶段的总期望成本模型及其最优(Q, R, SS)策略,进而结合算例给出模型的最优策略的数值解。     

基于动态模型平均的大豆期货价格预测研究

针对大豆期货价格波动的复杂性及影响因素的多元性,本文将动态模型平均理论引入大豆期货价格分析与预测研究中,通过动态选择解释变量和系数时变程度,在有效控制模型和系数不确定性的同时,最大限度综合利用大豆期货市场内外部信息,以提高大豆期货价格预测准确度。具体的,本文提出一套基于动态模型平均理论的大豆期货价格影响因素与预测分析框架,从期货市场和经济环境等两方面准确地识别出大豆期货价格影响因素的时变特征,进而构建大豆期货价格预测模型,并通过预测误差指标和Diebold-Mariano检验法评估其与基准模型的预测能力。研究结果表明,动态模型平均理论在有效剖析大豆期货价格影响因素的时变特征的同时,能明显提升大豆期货价格预测准确度。     

A diagnostic analysis of the impact of forecast errors on production planning via MRP system nervousness

Abstract. The purpose of this paper is to examine the impact of forecast errors on the performance of a multi-product, multilevel production planning system via MRP system nervousness. The accuracy of forecasting methods was at one time a major concern of production scheduling and inventory control. However, with the advent of material requirements planning (MRP) systems, the significance of selecting an accurate forecasting method has diminished. Inaccurate forecast results are taken as a fact of life in production planning. Instead of attempting to develop an accurate forecasting method, efforts have been devoted towards providing an appropriate buffering method ai the master production schedule level or on the shop floor level to counteract fluctuations in demand. MRP is capable of rescheduling planned orders as well as open orders to restore the priority integrity after the disruptive changes of forecast errors occur. Nevertheless, excessive rescheduling may lead to a problem, generally referred to as system nervousness. This study investigates this problem by means of a computer simulation model. The results show that the presence of forecasi     

政策冲击影响下中国能源消费预测分析及控制策略

本文运用缓冲算子和灰色GM（1,1）模型,对中国能源消费总量和单位GDP能耗进行了模拟和预测,在能源消费预测结果的基础上,构建了两种控制策略模型,并以中国单位GDP能耗预测为例进行了算例分析。研究结果表明,灰色模型较好地模拟和预测了中国能源消费总量和单位GDP能耗。中国在“十二五”期间的节能潜力很大,能顺利完成能源消费总量的指标。“十二五”安全控制策略为[0.48,1）,即国家在“十二五”期间的控制力度应调整为“十一五”控制力度的0.48~1倍之间。     

Economics and quantitative economics: A defence

The purpose of the paper is to discuss quantitative work in economics, especially in relation to problems of macro-economic policy. It is argued that many recent criticisms of economics are not well founded, and it is certainly not the case that what is wrong with economics is its quantitative bias. Quite the contrary; in a number of areas more quantitative work is required. The use of control theory methods should be welcomed by economists, but these need not be introduced to the exclusion of other methods. Finally, some suggestions are made for further research.     

Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems

This paper evaluates alternative methods of establishing the safety stock level taking into consideration of historical measures of forecasting accuracy and the needs for master production scheduling and material requirement planning under a rolling time horizon. A computer model is used to simulate the forecasting, master production scheduling and material planning activities in a company that produces to stock and the production activities are managed by multilevel MRP systems. The simulation output is analysed to evaluate the impact of safety stock methods on MRP system performance. The result of the study shows that using safety stock can help to reduce total cost, schedule instability and improve service level in the MRP systems. Guidelines are developed to help managers select methods to determine safety stock in MRP system operations.     

基于财政补贴的拥挤定价下公交收费策略研究

交通拥挤是目前世界上多数大中城市普遍面临的问题,公交优先和拥挤定价是针时该问题的有效管理措施.巨额的公交财政补贴,往往是我国城市推行公交优先政策的障碍.本文采用交通行为科学的理论和方法,从系统科学的角度将财政补贴、拥挤定价和公交收费等问题结合起来,设计了基于财政补贴的拥挤定价下的公交收费策略,解释了如何通过拥挤定价和公交收费等手段实现缓解交通压力减少公交财政补贴的管理目标.本文以广州市为典型,进行了相应的策略分析.     

Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis

Intermittent demand is characterized by occasional demand arrivals interspersed by time intervals during which no demand occurs. These demand patterns pose considerable difficulties in terms of forecasting and stock control due to their compound nature, which implies variability both in terms of demand arrivals and demand sizes. An intuitively appealing strategy to deal with such patterns from a forecasting and stock control perspective is to aggregate demand in lower-frequency ‘time buckets’, thereby reducing the presence of zero observations. In this paper, we investigate the impact of forecasting aggregation on the stock control performance of intermittent demand patterns. The benefit of the forecasting aggregation approach is empirically assessed by means of analysis on a large demand dataset from the Royal Air Force (UK). The results show that the aggregation forecasting approach results in higher achieved service levels as compared to the classical forecasting approach. Moreover, when the combined service-cost performance is considered, the results also show that the former approach is more efficient than the latter, especially for high target service levels.     

随机扰动下激励系统的分岔与控制研究

为研究激励系统分岔现象的控制问题,首先基于系统理论,分析激励系统的整体涌现性、非线性、鲁棒性和稳定性问题,运用随机非线形理论中的随机平均法和随机过程理论研究了激励系统的随机扰动和分岔问题,提出参数临界状态下,激励系统分岔平衡点稳定性方程,并进一步给出系统存在跨临界分岔和奇异诱导分岔的充分条件。最后,设计反馈控制器,通过控制能量和调整平衡点,抑制激励分岔,使系统趋于稳定。     

Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process

In this paper we study a hybrid system with both manufacturing and remanufacturing. The inventory control strategy we use in the manufacturing loop is an automatic pipeline, inventory and order based production control system (APIOBPCS). In the remanufacturing loop we employ a Kanban policy to represent a typical pull system. The methodology adopted uses control theory and simulation. The aim of the research is to analyse the dynamic (as distinct from the static) performance of the specified hybrid system. Dynamics have implications on total costs in terms of inventory holding, capacity utilisation and customer service failures. We analyse the parameter settings to find preferred “nominal”, “fast” and “slow” values in terms of system dynamics performance criteria such as rise time, settling time and overshoot. Based on these parameter settings, we investigate the robustness of the system to changes in return yield and the manufacturing/remanufacturing lead time. Our results clearly show that the system is robust with respect to the system dynamics performance and the remanufacturing process can help to improve system dynamics performance. Thus, the perceived benefits of remanufacturing of products, both environmentally and economically, as quoted in the literature are found not to be detrimental to system dynamics performance when a Kanban policy is used to control the remanufacturing process.     

Information distortion in a supply chain and its mitigation using soft computing approach

The information transferred in the form of orders between the nodes of a supply chain tends to be distorted when it moves from downstream to upstream. This phenomenon is called as bullwhip effect and this research is aimed to analyze this effect deeply in a single input single output (SISO) model. A discrete time series SISO model is developed for the analysis and it proves to be very useful in revealing the dynamics characteristics of the system. The bullwhip effect is measured from the transfer function model and the effect can be reduced by applying soft computing approach. A detailed sensitivity analysis is carried out to investigate the behavior of the model under various conditions. The applied fuzzy logic theory controls the errors and change in errors associated with forecasted demand between the nodes of a supply chain and it allows a smooth information flow in the chain. Tuning of fuzzy logic controller has been performed using adaptive neuro-fuzzy inference system (ANFIS). The method is illustrated with a numerical example. The application of soft computing approach addresses the real situation of human judgment with fuzziness helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.     

A reinforcement-learning approach for admission control in distributed network service systems

In the distributed network service systems such as streaming-media systems and resource-sharing systems with multiple service nodes, admission control (AC) technology is an essential way to enhance performance. Model-based optimization approaches are good ways to be applied to analyze and solve the optimal AC policy. However, due to “the curse of dimensionality”, computing such policy for practical systems is a rather difficult task. In this paper, we consider a general model of the distributed network service systems, and address the problem of designing an optimal AC policy. An analytical model is presented for the system with fixed parameters based on semi-Markov decision process (SMDP). We design an event-driven AC policy, and the stationary randomized policy is taken as the policy structure. To solve the SMDP, both the state aggregation approach and the reinforcement-learning (RL) method with online policy optimization algorithm are applied. Then, we extend the problem by considering the system with time-varying parameters, where the arrival rates of requests at each service node may change over time. In view of this situation, an AC policy switching mechanism is presented. This mechanism allows the system to decide whether to adjust its AC policy according to the policy switching rule. And in order to maximize the gain of system, that is, to obtain the optimal AC policy switching rule, another RL-based algorithm is applied. To assess the effectiveness of SMDP-based AC policy and policy switching mechanism for the system, numerical experiments are presented. We compare the performance of optimal policies obtained by the solutions of proposed methods with other classical AC policies. The simulation results illustrate that higher performance and computational efficiency could be achieved by using the SMDP model and RL-based algorithms proposed in this paper.