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，其中一些挑战可以说是由于单个建筑和居住在其中的人类之间的多样性而存在的。建筑物本质上是独特的系统，因此很难将技术解决方案推广到任何单个财产。相反，要为某一特定建筑的节能维护做出强有力的投资决策，需要一定程度的量身定制的工程和经济分析。要理解为什么会出现这种情况，人们只需要考虑在任意建筑中做出决策时可能需要解决的一系列问题。例如，我们可能会问：这栋建筑和目前安装的设备的年龄是多少？供暖系统是否需要更换？如果是，当前系统是锅炉吗？如果是，它的运行效率如何？该建筑是否会从新的锅炉或电热泵中受益？它会从更换供热分配管道中受益吗？这些技术的成本/收益是否取决于政府的关税和补贴？如果未来任何可用的补贴被削减，面临的风险是什么？这两项技术对未来天然气和电力价格的影响有多大？这个风险值得冒吗？开始考虑期权和相关风险是不是太昂贵了？设施管理人员如何可视化所有这些方面的可用选项和可能的利益和风险分布？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定，有证据表明，人们需要充分了解与每个单独项目相关的工程、经济和社会科学问题。到目前为止，获得这些信息被认为是一项代价高昂的工作，并促使人们普遍认为，大幅削减建筑能耗（每笔投资节省15%以上的能源）在经济上是一件冒险的事情。该提案首次开发并推荐了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。它将显著降低获取信息的成本，以进行稳健的改造和设施管理决策。这个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：(i)收集有关不确定性参数的建筑数据（即，“我们现在知道什么？”）；（ii）利用建筑模拟模型、从关键监测建筑工地获得的测量数据和前沿统计方法（即贝叶斯分析）传播和量化不确定性；（三）不确定性的显示和解释。在项目过程中，将组织研讨会来展示目前（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错误，以及建筑空间布局如何演变。图解风险信息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网站作为基于web的B-bem门户提供。

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.