B-bem: The Bayesian building energy management Portal

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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.