组合预测模型在能源消费预测中的应用

能源的需求预测是一个复杂的非线形系统,其发展变化具有增长性和波动性,组合预测对于信息不完备的复杂经济系统具有一定的实用性.本文利用我国能源消费的历史数据,采用灰色预测的GM(1,1)模型、BP神经网络模型和三次指数平滑模型进行优化组合,建立了能源消费组合预测模型,实证分析结果表明预测值和实际结果有很好的一致性,可以作为能源消费预测的有效工具.     

Using Spreadsheet Simulation to Generate a Distribution of Forecasts for Electric Power Demand

Electric utilities commonly use econometric modelling for energy and power forecasting. In order to accommodate the uncertainties contained in the input variables, such forecasts are frequently made in three parts: a base forecast, assumed to be the most likely, and a high and a low forecast, often arbitrarily spaced on either side of the base forecast, giving a band of possible values for the forecast. Usually, a single point value forecast is then utilized rather than a distribution of possible forecast values. This paper describes how commercially available spreadsheet software was utilized to convert an econometric energy forecast into probabilistic demand and energy forecasts that incorporate weather variation, as well as other uncertain inputs.     

Forecasting demand for single-period products: A case study in the apparel industry

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel ‘top-flop’ approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.     

电力需求预测管理信息系统

本文根据电力需求预测模型的特点，提出了能给预测过程提供信息支持和辅助决策的管理信息系统，并对系统进行了详细的分析和设计。     

Long term forecasting of energy demand in the developing countries

Forecasting long-term energy demand should be an important component of energy planning in developing countries. Traditional aggregated econometric metods are not well adapted and technico-economic approaches which look in more details at the determinants of energy demand seem better suited. This paper outlines a possible approach to the modeling of energy demand in developing countries.     

A hybrid spares demand forecasting method dedicated to mining industry

The paper addresses the problem of lumpy demand forecasting which is typical for spare parts. Several prediction methods are presented in the paper - traditional techniques based on time series and advanced methods which use artificial neural networks. The paper presents a new hybrid spares demand forecasting method dedicated to mining companies. The method combines information criteria, regression modeling and artificial neural networks. The paper also discusses simulation research related to efficiency assessment of the chosen variable selection methods and its application in the newly developed forecasting method. The assessment of this method is conducted by a comparison with traditional methods and is based on selected forecast errors.     

Using imperfect advance demand information in forecasting

Email: t.tan{at}tue.nl Received on 4 January 2007. Accepted on 11 January 2008. In this paper, we consider the demand-forecasting problem ofa make-to-stock system operating in a business-to-business environmentwhere some customers provide information on their future orders,which are subject to changes in time, hence constituting imperfectadvance demand information (ADI). The demand is highly volatileand non-stationary not only because it is subject to seasonalityand changing trends but also because some individual clientdemands have significant influence on the total demand. In suchan environment, traditional forecasting methods may result inhighly inaccurate forecasts, since they are mostly developedfor the total demand based only on the demand history, not makinguse of demand information and ignoring the effects of individualorder patterns of the customers. We propose a forecasting methodologythat makes use of individual ordering pattern histories of theproduct–customer combinations and the current build upof orders. Moreover, we propose making use of limited judgementalupdates on the statistical forecasts prior to the use of ADI.     

中美双边航空运输市场组合预测研究

预测应用研究表明,组合预测可以综合利用各单项预测方法所提供的信息,是提高预测精度的有效途径.本文在平均发展速度预测法、指数趋势预测以及灰色预测方法的基础上建立组合预测模型,采用熵值法确定组合权系数,预测了2006年至2010年中美间航空运输周转量、中美间航空客运量及货运量.     

SIOPRED: a prediction and optimisation integrated system for demand

This paper presents a forecasting support system based on the generalised Holt-Winters exponential smoothing scheme to forecast time series of levels of demand. It is conceived as an integrated tool which has been implemented in Visual Basic. For improving the accuracy of automatic forecasting it uses an optimisation-based scheme which unifies the stages of estimation of the parameters and model selection. Based on this scheme, suitable forecasts and prediction intervals are obtained. The performance of the proposed system is compared with a number of well-established automatic forecasting procedures with respect to the 3003 time series included in the M3-competition.      

Forecasting the value effect of seasoned equity offering announcements

Seasoned Equity Offers (SEOs) by publicly listed firms generally result in unexpected negative share price returns, being often perceived as a signal of overvalued share prices and information asymmetries. Hence, forecasting the value effect of such announcements is of crucial importance for issuers, who wish to avoid share price dilution, but also for professional fund managers and individual investors alike. This study adopts the OR forecasting paradigm, where the latest part of the data is used as a holdout, on which a competition is performed unveiling the most effective forecasting techniques for the matter in question. We employ data from a European Market raising in total €8 billion through 149 SEOs. We compare economic and econometric models to forecasting techniques mostly applied in the OR literature such as Nearest Neighbour approaches, Artificial Neural Networks as well as human Judgment. Evaluation in terms of statistical accuracy metrics indicates the superiority of the econometric models, while economic evaluation based on trading strategies and simulated profits attests expert judgement and nearest-neighbour approaches as top performers.     

A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting

In some countries that energy prices are low, price elasticity of demand may not be significant. In this case, large increase or hike in energy prices may impact energy consumption in a way which cannot be drawn from historical data. This paper proposes an integrated adaptive fuzzy inference system (FIS) to forecast long-term natural gas (NG) consumption when prices experience large increase. To incorporate the impact of price hike into modeling, a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Linear regressions are used to construct a first order TS-FIS. Furthermore, adaptive network-based FIS (ANFIS) is used to forecast NG consumption in power plants. To cope with random uncertainty in small historical data sets, Monte Carlo simulation is utilized to generate training data for ANFIS. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual NG consumption in Iran where removing energy subsidies has resulted in a hike in NG prices.     

MODELING OF A BLOCK TARIFF SYSTEM FOR GROUNDWATER REGULATION

The block tariff system is widely used by public utility companies and governments. Because the unit rate is determined at the same time as are choice variables, the resulting endogeneity has been the subject of studies on residential water demand and in labor economics. Without discussing any mechanism that helps people come to their final decisions, these studies rely on observed data and econometric tools to estimate parameters in demand or supply functions. Because their main concern is the amount of resources devoted to a prespecified activity, their methodologies cannot analyze problems in which resources could flow to more than one outlet. This study constructs a computable framework that can deal with the endogeneity issue and help allocate scarce resources to mutually exclusive activities. Using a block tariff system for regulating groundwater extraction by aquaculture farmers in Taiwan as an example, we will show how the government and farmers can rely on the proposed framework to meet their interests.     

Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains

Supply chain inventories are prone to fluctuations and instability. Known as the bullwhip effect, small variations in the end item demand create oscillations that amplify throughout the chain. By using system dynamics simulation, we investigate some of the structural sources of the bullwhip effect, and explore the effectiveness of information sharing to eliminate the undesirable fluctuations. Extensive simulation analysis is carried out on parameters of some standard ordering policies, as well as external demand and lead-time parameters. Simulation results show that (i) a major structural cause of the bullwhip effect is isolated demand forecasting performed at each echelon of the supply chain, and (ii) demand and forecast sharing strategies can significantly reduce the bullwhip effect, even though they cannot completely eliminate it. We specifically show how each policy is improved by demand and forecast sharing. Future research involves more advanced ordering and forecasting methods, modelling of other well-known sources of bullwhip, and more complex supply network structures.     

A collaborative decentralized distribution system with demand forecast updates

In this paper, we study inventory pooling coalitions within a decentralized distribution system consisting of a manufacturer, a warehouse (or an integration center), and n retailers. At the time their orders are placed, the retailers know their demand distribution but do not know the exact value of the demand. After certain production and transportation lead time elapses, the orders arrive at the warehouse. During this time, the retailers can update their demand forecasts.We first focus on cooperation among the retailers - the retailers coordinate their initial orders and can reallocate their orders in the warehouse after they receive more information about their demand and update their demand forecasts. We study two types of cooperation: forecast sharing and joint forecasting. By using an example, we illustrate how forecast sharing collaboration might worsen performance, and asymmetric forecasting capabilities of the retailers might harm the cooperation. However, this does not happen if the retailers possess symmetric forecasting capabilities or they cooperate by joint forecasting, and the associated cooperative games have non-empty cores.Finally, we analyze the impact that cooperation and non-cooperation of the retailers has on the manufacturer’s profit. We focus on coordination of the entire supply chain through a three-parameter buyback contract. We show that our three-parameter contract can coordinate the system if the retailers have symmetric margins. Moreover, under such a contract the manufacturer benefits from retailers’ cooperation since he can get a share of improved performance.     

Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al (2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24?h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models (according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combination of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to December 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.     

Forecasting energy demand using neural-network-based grey residual modification models

Grey forecasting models have taken an important role for forecasting energy demand, particularly the GM(1,1) model, because they are able to construct a forecasting model using a limited samples without statistical assumptions. To improve prediction accuracy of a GM(1,1) model, its predicted values are often adjusted by establishing a residual GM(1,1) model, which together form a grey residual modification model. Two main issues should be considered: the sign estimation for a predicted residual and the way the two models are constructed. Previous studies have concentrated on the former issue. However, since both models are usually established in the traditional manner, which is dependent on a specific parameter that is not easily determined, this paper focuses on the latter issue, incorporating the neural-network-based GM(1,1) model into a residual modification model to resolve the drawback. Prediction accuracies of the proposed neural-network-based prediction models were verified using real power and energy demand cases. Experimental results verify that the proposed prediction models perform well in comparison with original ones.     

带有风能的智能电网多周期买电策略研究

本文研究带有风能随机供给的智能电网中传统能源的多周期买电问题,假设存在一个能源运营商集中负责智能电网传统能源的购买和消费。通过构建并求解动态规划模型,找到能源运营商在风能供给不确定性下的传统能源最优多周期买电策略。在最优买电策略下,能源运营商只有在当期电价足够小时才购买传统能源,其买电量与风能分布、价格信息和时间信息有关。在实际数据的基础之上,提供详实的数值实验对比研究了本文的最优买电策略和其他两种策略(实践中只考虑风能估计的策略和放弃利用风能的策略)在最小化总成本方面的效果,并验证了本文的最优买电策略在真实风能数据中的鲁棒性。     

关于湖北省节能的投入产出分析

运用投入产出法,对湖北省达成"十一五"节能目标的万元GDP综合能耗进行了分能源品种的分解,对湖北省未来五年的能源经济指标进行了预测和分析.首先,本文把64部门的2002年湖北省投入产出表整理成6部门的投入产出表,推测出2005年湖北省能源生产和消费指标,分析了湖北省能源供需的基本形势.其次,本文以实现"十一五"期间万元GDP综合能耗降低20%为目标,对不同GDP增长率下的能源需求情景进行了预测,计算了湖北省2010年分品种的万元GDP综合能耗、能源消费弹性系数等指标,分析了不同能源品种对单位GDP综合能耗下降的贡献程度.最后本文提出了政策建议,并指出模型的发展方向.     

A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast

Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.