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物：加热，冷却和照明系统的能源使用，内部温度体验以及潜在的室内空气质量。适应不断上升的温度可能会增加对冷却系统的需求：如果这是由传统的空调来满足，将增加二氧化碳排放量，这将加剧这种情况。气候变化还可能损害传统和创新被动设计解决方案的可行性，改变采光使用的平衡，并引发建筑结构和系统的改造。动态模拟模型（DSM）以及基于“人工方法”的计算程序是建筑物能源和舒适度设计和分析的关键资源：这些程序和程序已成为公认的，在某些情况下，是建筑设计和分析过程的强制性部分。DSM从时间序列输入文件（通常每小时）工作，因此本质上是确定性的。最近，它已被证明，气候变化对能源消耗和热舒适的各种建筑物的影响，可以预测来自UKCIP02气候变化情景的天气数据。未来以概率形式提供此类情景（UKCIP08）既带来了机遇，也带来了挑战：提供一个更灵活的决策框架的机会，但是，如何将新的情景与现有的模型有效地结合起来，为适应决策提供明确的信息，这是一个挑战。这个为期两年的项目旨在通过结合案例研究来解决这两个问题，基于UKCIP08新情景输出的表格和每小时天气数据的开发的建模。该项目小组将由德蒙福特大学能源和可持续发展研究所和东安格利亚大学气候研究所组成，并与奥雅纳合作。项目管理将围绕定期举办的一系列利益攸关方讲习班进行，这些讲习班将在塑造项目合作伙伴的工作方面发挥关键作用，第一阶段（约一年）将侧重于与利用概率数据建立建筑物模型有关的技术挑战。一个关键的问题是，从气候假设情景产生的概率密度函数中制定抽样方法，以形成需求侧模型所需的投入。这项工作将建立在提案人以前开展的项目所取得的进展的基础上，并与一些正在进行的重要研究项目有关，如CaRB和TARBASE。第二阶段的工作将在UKCIP08数据可用后继续进行，并将探索向需求监测机制提供投入的最佳方式，以及调整产出以通报适应决策的有效手段。将直接比较BETWIXT和UKCIP02项目等采用的确定性方法与较新的概率方法（CRANIUM和UKCIP08项目）。该项目的主要成果将是改进建筑性能分析模拟方法，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.