The use of probabilistic climate scenarios in building environmental performance simulation

气候变化将以许多不同的方式影响建筑：供暖、制冷和照明系统的能源使用，室内温度体验，以及潜在的室内空气质量。适应不断升高的温度可能会增加对冷却系统的需求：如果通过传统空调来满足这一需求，二氧化碳排放量将会增加，这将加剧这种情况。气候变化还可能损害传统和创新的被动式设计解决方案的可行性，改变采光的使用平衡，并引发建筑结构和系统的翻新。动态模拟模型(DSM)，以及基于“手动方法”的计算程序是设计和分析建筑中能量和舒适度的关键资源：此类程序和程序已成为公认的程序和程序，在某些情况下，还成为建筑设计和分析过程中的强制性部分。DSM从时间序列输入文件(通常是每小时)工作，因此本质上是确定性的。最近已经证明，气候变化对各种建筑的能源消耗和热舒适性的影响可以使用来自UKCIP02气候变化情景的天气数据进行预测。未来以概率形式提供这类情景(UKCIP08)既是机遇，也是挑战：为决策提供更灵活的框架的机会，但如何有效地将新情景与现有模型对接，以便为适应决策提供明确信息方面的挑战。这个为期两年的项目旨在通过将基于案例研究的建模与根据新的UKCIP08情景的输出产生的表格和每小时天气数据相结合来解决这两个问题。该项目团队将由德蒙福特大学能源与可持续发展研究所和东安格利亚大学气候研究组与奥雅纳公司合作组成。项目管理将围绕一系列定期举办的利益攸关方讲习班进行，这些讲习班将在确定项目伙伴的工作方面发挥关键作用。第一阶段(大约一年)将侧重于与使用概率数据建模建筑物有关的技术挑战。一个关键问题将是制定从气候情景中产生的概率密度函数进行抽样的方法，以便形成决策支持系统所需的输入。这项工作将在提出者以前开展的项目中取得的进展的基础上进行，并与一些重要的正在进行的研究项目有关，如CARB和TARBASE。第二阶段的工作将从UKCIP08数据的可获得性开始，并将探索向监测系统提供投入的最佳方式，以及调整产出以便为适应决策提供信息的有效手段。将直接比较在t和UKCIP02项目中采用的确定性方法和较新的概率性方法(CRIAL和UKCIP08项目)。该项目的主要成果将是改进进行建筑性能分析模拟的方法，以便在必须考虑重大不确定性的情况下为设计和适应决策提供信息。

Climate change will impact on buildings in many different ways: on energy use in heating, cooling and lighting systems, on the internal temperature experience and, potentially, on indoor air quality. Adapting to increasing temperatures may increase demand on cooling systems: if this were to be met by conventional air-conditioning there would be increased carbon dioxide emissions which would exacerbate the situation. Climate change may also compromise the viability of traditional and innovative passive design solutions, alter the balance of the use of daylighting and trigger retrofitting of building fabric and systems. It is imperative that such developments do not increase the carbon footprints of buildings.Dynamic simulation models (DSM), together with calculation procedures based on 'manual methods' are a key resource for the design and analysis of energy and comfort in buildings: such programs and procedures have become an accepted and, in some situations, a mandatory part of the building design and analysis process. DSMs work from a time-series input file (normally hourly) and hence are deterministic in nature. Recently it has been demonstrated that the effects of climate change on energy consumption and thermal comfort for a variety of buildings can be predicted using weather data derived from the UKCIP02 climate change scenarios. The future availability of such scenarios in a probabilistic form (UKCIP08) presents both an opportunity and a challenge: the opportunity to provide a more flexible framework for decision-making, but a challenge in how the new scenarios can be effectively interfaced with currently available models to provide clear information with which to inform adaptation decisions.This two-year project aims to address both these issues by combining case study-based modelling with the development of both tabular and hourly weather data produced from the output of the new UKCIP08 scenarios. The project team will consist of the Institute of Energy and Sustainable Development, De Montfort University and the Climatic Research Unit, University of East Anglia, in partnership with Arup. Project management will be structured around a regular series of stakeholder workshops, which will play a key role in shaping the work of the project partners.The first phase (approximately one year) will focus on technical challenges relating to modelling buildings with probabilistic data. A key issue will be the development of methods of sampling from the probability density functions that will be produced from the climate scenarios, in order to form the inputs required by DSMs. This work will build on progress made in previous projects carried out by the proposers and also relate to a number of significant on-going research projects such as CaRB and TARBASE. The second phase of the work will follow on from the availability of the UKCIP08 data and will explore optimal ways to provide inputs to the DSMs and effective means of tailoring the outputs to inform adaptation decision-making. A direct comparison will be made between the deterministic approach adopted, for example, in the BETWIXT and UKCIP02 projects, and the newer, probabilistic methods (the CRANIUM and UKCIP08 projects). The primary outcome of this project will be improved methodologies for carrying out building performance analysis simulations, in order to inform design and adaptation decisions in situations where significant uncertainties must be accounted for.