B-bem: The Bayesian building energy management Portal

现有非住宅建筑的能源管理面临着许多挑战，其中一些可以说是存在的，因为单个建筑物和居住它们的人之间存在着多样性。建筑物本身就是独特的系统，因此很难为任何单个物业推广技术解决方案。相反，要为特定建筑的节能维护做出稳健的投资决策，需要一定程度的量身定制工程和经济分析。要理解为什么会出现这种情况，我们只需要考虑在任意建筑中进行决策时可能需要解决的一系列问题。例如，我们可能会问：建筑物的年龄和目前安装在其中的设备是什么？供暖系统需要更换吗？如果是的话，目前的系统是一个锅炉，如果是的话，它的效率如何？这座建筑会受益于一个新的锅炉或电热泵吗？它会受益于更换供暖分配管道吗？这些技术的成本/效益是否取决于政府的关税和补贴？如果未来削减任何现有补贴，将面临什么风险？这两种技术对未来天然气和电力价格的影响有多大？值得冒这个险吗？甚至开始考虑选择和相关风险是否太昂贵了？设施经理如何设想所有这些方面的可用选项以及可能的收益和风险分布？该项目旨在应对这些挑战。事实上，为了对未来的建筑运营和技术投资做出合理的决策，有证据表明，人们需要充分了解与每个项目有关的工程、经济和社会科学问题。到目前为止，获得这些信息被视为一项昂贵的工作，并导致人们普遍认为，大幅削减建筑能耗（每笔投资节省15%以上的能源）是一件经济上有风险的事情。该提案是第一个开发和推荐一种全新的方法来执行建筑审计，能源模拟，不确定性分析，数据可视化以及最终的投资决策。该项目的直接产出将是一系列软件工具，用于三个不同但相关的目的：㈠收集有关不确定性参数的建筑数据（即，“我们现在知道什么了？");（ii）使用建筑物模拟模型、从主要监测建筑工地获得的测量结果和尖端统计方法（即，贝叶斯分析）;及（iii）不确定性的显示和解释。在该项目的过程中，将组织研讨会，以展示目前（不确定）的知识，这些知识到目前为止在建筑领域基本上没有记录，能源研究界也无法获得。这包括了解在管理传统能源系统中观察到的最常见故障，以及建筑物的空间布局如何演变。风险信息的图示和了解用户对不确定性和风险的看法将是这些讲习班和研究方案的关键内容。我们的软件工具、用户指南和测试用例的数值运行将通过剑桥大学的网站作为基于网络的B-BEM门户网站提供。

Energy Management of existing non-domestic buildings is wrought with many challenges, a number of which arguably exist due to the diversity found amongst individual buildings and amongst the humans who occupy them. Buildings are inherently unique systems making it difficult to generalize technology solutions for any individual property. Instead, to make robust investment decisions for the energy-efficient upkeep of a particular building requires some degree of tailored engineering and economic analysis. To understand why this is the case, one need only to consider the chain of questions one would likely need to address for decision-making in an arbitrary building. For instance, we might ask: what is the age of the building and the equipment currently installed in it? Does the heating system need to be replaced? If yes, is the current system a boiler, and if so, how efficiently does it perform? Would the building benefit from a new boiler or an electric heat pump? Would it benefit from replacing the heating distribution pipes? Do the cost / benefits of any of these technologies depend on government tariffs and subsidies? What is the risk faced if any available subsidies are cut in the future? How robust is either technology to the future price of natural gas and electricity? Would that risk be worth taking? Is it too expensive to even start thinking about the options and associated risks? How would a facility manager visualise the options available and possible spreads of benefits and risks for all these aspects?This project aims to respond to these challenges. Indeed, in order to make sound decisions on future building operation and technology investment, evidence shows that one needs adequate information on a number of engineering, economics, and social science matters pertaining to each individual project. To obtain this information has so-far been viewed as a costly exercise, and has contributed to the general perception that undertaking deep cuts to building energy consumption (achieving more than 15% in energy savings per investment) is an economically risky affair. This proposal is the first to develop and recommend an altogether new approach to performing building audits, energy simulation, uncertainty analysis, data visualization, and finally investment decision-making. It will lead to a marked reduction in the cost of acquiring information for robust retrofit and facility management decisions.The direct outputs of this project will be a series of software tools for three distinct but related purposes: (i) collecting building data on relevant uncertainty parameters (i.e., "what do we know now?"); (ii) propagating and quantifying uncertainty using building simulation models, measurements obtained from key monitored building sites, and cutting-edge statistical approaches (i.e., Bayesian analysis); and (iii) the display and interpretation of uncertainty. During the course of the project, workshops will be organised to lay out the current (uncertain) knowledge that has been, until now, largely undocumented in the buildings sector and inaccessible to the energy research community. This includes gaining understanding on the most common faults observed in managing conventional energy systems, and how spatial layouts in building evolve. The graphical presentation of risk information and understanding users' perception of uncertainty and risk will be key elements of these workshops and the research programme. Our software tools, user guidance, and numerical runs of test cases will be made available, as the web-based B-bem portal, via the University of Cambridge web site.