Numerical Optimisation of Building Thermal and Energy Performance in Hospitals
Numerical Optimisation of Building Thermal and Energy Performance in Hospitals
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发表时间：
2017-03
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通讯作者：
A. Cowie
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作者：
A. Cowie
本论文详细介绍了开发和测试的元模型为基础的建筑优化方法称为热建筑优化工具（T-BOT），设计作为一个信息收集框架和决策支持工具，而不是一个设计自动化。建筑模拟的初始样本用于训练设计空间的移动最小二乘回归（MLSR）元模型。遗传算法（GA），然后使用优化的双重目标，最大限度地减少时间平均的热不适和能源使用。最佳的权衡是作为一个帕累托前沿。建筑模拟程序ESP-r的自适应耦合功能用于增强动态热模型（DTM）与计算流体动力学（CFD），允许室内热舒适性的局部评估。此外，利用元建模引起的模拟和优化之间的脱节，以提供灵活性，在初始样本中收集的数据。因此，可以从一组样本模拟中对位置、时间段、热舒适标准和设计变量的任何组合进行优化;这被称为“一个样本多个优化”或奥斯莫方法。这可以比可比的直接搜索优化技术节省大量时间。据作者所知，奥斯莫方法和DTM与CFD的自适应耦合是建筑热优化（BTO）模型中的独特之处。开发和测试集中在医院环境中，尽管该方法可能适用于其他环境。该程序进行了测试，应用到两个模型，一个是理论测试的情况下，一个基于真实的医院建筑的案例研究。结果发现，在空间位置，时间段和热舒适标准的变化可以导致不同的最佳条件，虽然季节变化有很大的影响。此外，样本量和设计变量的选择及其范围被认为是元模型保真度的关键。
This thesis details the development and testing of a metamodel-based building optimisation methodology dubbed thermal building optimisation tool (T-BOT), designed as an information gathering framework and decision support tool rather than a design automator. Initial samples of building simulations are used to train moving least squares regression (MLSR) meta-models of the design space. A genetic algorithm (GA) is then used to optimise with the dual objectives of minimising time-averaged thermal discomfort and energy use. The optimum trade-off is presented as a Pareto front. Adaptive coupling functionality of the building simulation program ESP-r is used to augment the dynamic thermal model (DTM) with computational fluid dynamics (CFD), allowing local evaluation of thermal comfort within rooms. Furthermore, the disconnect between simulation and optimisation induced by the metamodeling is exploited to lend flexibility to the data gathered in the initial samples. Optimisations can hence be performed for any combination of location, time period, thermal comfort criteria and design variables, from a single set of sample simulations; this was termed a “one sample many optimisations” or OSMO approach. This can present substantial time savings over a comparable direct search optimisation technique. To the author’s knowledge the OSMO approach and adaptive coupling of DTM and CFD are unique among building thermal optimisation (BTO) models. Development and testing was focussed on hospital environments, though the method is potentially applicable to other environments. The program was tested by application to two models, one a theoretical test case and one a case study based on a real hospital building. It was found that variation in spatial location, time period and thermal comfort criteria can result in different optimum conditions, though seasonal variation had a large effect on this. Also the sample size and selection of design variables and their ranges were found to be critical to meta-model fidelity.