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MATHEMATICAL PROGRAMMING BASIS FOR DECISION MAKING USING WEATHER AND CLIMATE INFORMATION FOR THE ENERGY SECTOR

机译：使用天气和气候信息进行能源决策的数学编程基础

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Decision making (DM) problem is of great practical value in many areas of human activities. Most widely used DM methods are based on probabilistic approaches. Well-known Bayesian theorem for conditional probability density function (PDF) is a background for such techniques. It is due to some uncertainty in many parameters entered in any model described functioning of many real systems or objects. Uncertainty in our knowledge might be expressed in alternative form. I offer to employ appropriate confidential intervals for model parameters instead of relevant PDF. Thus one can formulate a prior uncertainty in model parameters by means of a set of linear constraints. Related cost or goal function should be defined at corresponding set of parameters. That leads us to statement of problem in terms of operational research or mathematical linear programming. It is more convenient to formulate such optimization problem for discreet or Boolean variables. Review of relevant problem statements and numerical techniques will be presented as well as several examples. The house heating and air condition optimal strategies responded to different IPCC climate change scenarios for some domains of Russia are considered. Evolving of climate and energy costs should be taken into account in building construction design. Optimal relationship between future expenses for house heating and costs of new house constructions including material costs and its amounts is a subject of discussion. In both considered tasks DM might be performed by means of the discreet optimization algorithms. If the DM variables are all required to be integers, then the problem is called an integer programming (IP). The "0-1" IP is the special case of integer programming where variables are required to be 0 or 1 (rather than arbitrary integers). The IP is a most convenient form for decision maker use. The "1" value means that a given scenarios is accepted, the "0" value means that a given scenarios is rejected. To illustrate suggested approach the "branch and bound" technique was implemented to seasonal surface atmosphere temperature ensemble predictions system (EPS) for northern parts of Russia. Aim of this illustrative research was to link the EPS output facility to requirements of particular users.
机译：决策（DM）问题在人类活动的许多领域都具有很大的实用价值。最广泛使用的DM方法是基于概率方法的。用于条件概率密度函数（PDF）的著名贝叶斯定理是此类技术的背景。这是由于在描述许多实际系统或对象的功能的任何模型中输入的许多参数存在某些不确定性。我们知识的不确定性可以用其他形式表示。我提供为模型参数采用适当的机密间隔，而不是相关的PDF。因此，可以通过一组线性约束来表示模型参数的先验不确定性。相关的成本或目标函数应在相应的参数集中定义。这导致我们从运筹学或数学线性规划的角度陈述问题。为离散变量或布尔变量制定这种优化问题更为方便。将介绍相关的问题陈述和数值技术，以及一些示例。考虑了针对俄罗斯某些地区的不同IPCC气候变化情景的房屋采暖和空调最佳策略。在建筑设计中应考虑气候和能源成本的变化。未来房屋取暖费用与新房屋建设成本（包括材料成本及其金额）之间的最佳关系是讨论的主题。在两种考虑的任务中，都可以通过谨慎的优化算法来执行DM。如果所有DM变量都必须是整数，则该问题称为整数编程（IP）。 IP“ 0-1”是整数编程的特殊情况，其中变量必须为0或1（而不是任意整数）。 IP是决策者使用的最方便的形式。 “ 1”值表示接受给定方案，“ 0”值表示拒绝给定方案。为了说明建议的方法，对俄罗斯北部地区的季节性地表温度总体预报系统（EPS）实施了“分支约束”技术。这项说明性研究的目的是将EPS输出设施与特定用户的需求联系起来。

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