Quantification of model form uncertainty in the calculation of solar diffuse irradiation on inclined surfaces for building energy simulation

Traditional uncertainty quantification (UQ) in the prediction of building energy consumption has been limited to the propagation of uncertainties in model input parameters. Models by definition ignore, at least to some degree, and, in almost all cases, simplify the physical processes that govern the reality of interest, thereby introducing additional uncertainty in model predictions that cannot be captured as input parameter uncertainty. Quantification of this type of uncertainty (which we will refer to as model form uncertainty) is a necessary step towards the complete UQ of model predictions. This paper introduces a general framework for model form UQ and shows its application to the widely used sky irradiation model developed by Perez et al. [1990. “Modeling Daylight Availability and Irradiance Components from Direct and Global Irradiance.” Solar Energy 44 (5): 271–289], which computes solar diffuse irradiation on inclined surfaces. We collected a data set of one-year measurements of solar irradiation at one location in the USA. The measurements were done at surfaces with different tilt angles and orientations, for a wide spectrum of sky conditions. A statistical analysis using both this data set and published studies worldwide suggests that the Perez model performs non-uniformly across different locations and produces a certain bias in its predictions. Based on the same data, we then use a two-phase regression model to express model form uncertainty in the use of the Perez model at this particular location. Using a holdout validation test, we demonstrate that the two-phase regression model considerably reduces the model bias errors and root mean square errors for every tilted surface. Lastly, we discuss the significance of including model form uncertainty in the energy consumption predictions obtained with whole building simulation.     

方案设计阶段建筑性能模拟方法综述

从模拟辅助设计的过程分析、建筑群性能模拟、建筑单体性能模拟和建筑性能评价方法四个方面回顾了建筑方案设计阶段节能设计的研究成果,分析了现有研究方法的不足,指出解决现有问题应首先研究方案设计阶段的特点,其次分析模拟输入参数的可获得性,并在模拟中引入不确定性分析。     

建筑高度对周围风场环境影响的数值模拟

建筑风环境作为建筑节能设计的一个重要方面,其对于区域气候与局部微气候的影响都不容忽视。本文应用基于RNG k-ε模型的CFD软件数值模拟研究不同的建筑高度差对建筑周围风场环境的影响,揭示有利于风流动的建筑因素,为进一步的住区建筑规划提供思路。模拟结果显示,在来流风速一定双体建筑高度差不同时,建筑间谷区风速与形成涡旋的中心位置和大小均不同。     

Integrating neural network models with computational fluid dynamics (CFD) for site-specific wind condition

Most building energy simulations tend to neglect microclimates in building and system design, concentrating instead on building and system efficiency. Energy simulations utilize various outdoor variables from weather data, typically from the average weather record of the nearest weather station that is located in an open field, near airports and parks. The weather data may not accurately represent the physical microclimate of the site, and may therefore reduce the accuracy of simulation results. For this reason, this paper investigates utilizing computational fluid dynamics (CFD) with neural network (NN) model to predict site-specific wind parameters for energy simulation. The CFD simulation is used to find selected samples of site-specific wind conditions. Findings from CFD simulation are used as training data for NN. A trained NN predicts site-specific hourly wind conditions for a typical year. The outcome of the site-specific wind condition from the neural network is used as wind condition input for the energy simulation. The results of energy simulation using typical weather station data and site-specific weather data are compared in this paper, in order to find the possibility of using site-specific weather condition by NN with CFD to yield more realistic and robust ES results.     

拟建结构和现有结构的抗力概率模型

完善了结构抗力的随机过程概率模型,对独立增量过程概率模型作了更深入的阐述,并论述了抗力概率模型应满足的基本条件。针对拟建结构和现有结构之间的差别,文中还建立了不同结构具体的抗力概率模型,在现有结构抗力的概率模型中引入了未确知量,以反映人们在认识现有结构当前抗力时的未确知性。文中的抗力概率模型不仅解决了自相关系数难以统计的困难,而且在结构可靠性的分析中可以得到偏于保守的结果。     

An arctic low-energy house as experimental setup for studies of heat dynamics of buildings

This paper addresses the difficulties in pinpointing reasons for unexpectedly high energy consumption in construction, and in low-energy houses especially. Statistical methods are applied to improve the insight into the energy performance and heat dynamics of a building based on consumption records and weather data. Dynamical methods separate influences from outdoor temperature, solar radiation, and wind on the energy consumption in the building. The studied building is a low-energy house in Sisimiut, Greenland. Weather conditions like large temperature differences between indoors and outdoors throughout long winters, strong winds, and very different circumstances regarding solar radiation compared to areas where low-energy houses are usually built, make the location very interesting for modeling and testing purposes. In 2011 new measurement equipment was installed in the house, which will be used to develop more detailed models of the heat dynamics and energy performance in relation to different meteorological variables, heating systems, and user behavior. This type of models is known as a graybox model and is been introduced in this paper.     

Relationship between built form and energy performance of office buildings in a severe cold Chinese region

It is well recognized that building form has significant influences on energy performance in buildings, especially in the cold climate. It is imperative to understand the relationship between built forms and energy use in order to provide guidance in early project stage such as preliminary design. Therefore, this study focuses on two aspects to understand characteristics of energy use due to the change of parameters related to building form. The first aspect is to apply new metamodel global sensitivity analysis to determine key factors influencing energy use and the second aspect is to develop reliable fast-computing statistical models using state-of-art machine learning methods. An office building, located in Harbin, China, is chosen as a case study using EnergyPlus simulation program. The results indicate that non-linear relationships exist between input variables and energy use for both heating and electricity use. For heating energy, two factors (floor numbers and building scale) show a non-linear yet monotonic trend. For electricity use intensity, building scale is the only significant factor that has non-linear effects. It is also found that the ranking results of critical factors to both electricity use and heating energy per floor area vary significantly between small and large scale buildings. Neural network model performs better than other machine-learning methods, including ordinary linear model, MARS (multivariate adaptive regression splines), bagging MARS, support vector machine, random forest, and Gaussian process.     

半干旱区城市广场树木形态对微气候的影响研究

植物群落是改善微气候的重要影响因素,是微气候变化的驱动力之一,它对微气候的形成、变化等进程的影响已成为当前研究的重要组成部分。以半干旱区城市呼和浩特成吉思汗广场中4种不同植物群落类型的样地和1块空地为试验地,进行风速、温度、湿度实测和ENVI-met软件模拟分析,以评估各样地对微气候的影响。研究结果表明:植物群落调节微气候具有明显的季节性差异,且与树木胸径、高度、郁闭度、叶面积指数有明显相关性;乔木高度、形状、胸径可直接影响微气候环境,25 m高、心形、中等胸径的落叶乔木在夏季的降温、增湿、通风作用最明显;5m高、圆柱形、小胸径的落叶乔木在春、秋、冬3季增温、增湿、降风效果最优。揭示了广场植物群落数量属性与微气候变量之间的联系,确定了关键微气候变量在各植物群落中的变化规律,此结果可为半干旱地区广场绿色空间的植物种植设计提供参考及依据。     

碳化概率模型及混凝土结构碳化失效概率分析

混凝土碳化分析是评估混凝土结构长期性能的重要内容。该文分析总结了现有混凝土碳化理论和经验模型。在现有确定性模型基础上,通过添加模型修正项的方法修正确定性模型的误差,建立混凝土碳化概率模型。概率模型中模型参数的后验统计分布特征利用已有检测数据通过贝叶斯更新手段获得。基于建立的碳化概率模型,构建混凝土结构碳化时变失效概率的分析方法,该方法充分考虑模型不确定性对混凝土结构碳化时变失效概率的影响。运用该方法对一钢筋混凝土简支板的碳化性能进行了分析评估。     

树冠透射率对微气候和建筑能耗的 综合影响

现阶段中国城镇化率已超60%,城镇建筑运行能耗约占全国能源消耗总量的16.5%。老旧小区作为城镇建筑的重要存量,推动其绿化改造对改善建筑周围微气候和降低建筑能耗具有重大意义。目前,微气候和建筑能耗的模拟仿真分别基于不同的应用软件,且进行能耗模拟时并未考虑微气候因素对建筑能耗的影响。为定量评价和预测住区绿化改造对建筑周围微气候和建筑能耗的综合影响,基于Grasshopper平台,集合了微气候软件ENVI-met和建筑能耗软件EnergyPlus的模拟计算内核,开发了一种基于单平台耦合2类性能分析算法的协同工作流。研究结果表明,改变树冠透射率可使老旧小区建筑周围局部微气候的时空分布发生明显变化,从而显著改变建筑各楼层的夏季制冷能耗需求;长沙市城镇老旧小区绿化改造宜种植树冠透射率达0.05的树种,可使住宅建筑夏季制冷能耗日降幅最高至29.49kWh。     

低矮房屋风致结构响应极值不确定性分析

风致结构响应极值估算在结构抗风的可靠度设计中十分重要。在整个极值估算过程中,由于许多不定或随机的因素存在(如：极值自身、估算方法、样本采集、极值概率模型等),得到的极值通常存在不确定性。在各种影响因素中,该文将考虑结构响应极值变量本身的随机特性,对任意分位点处响应极值的不确定性进行分析。首先,利用有限元软件对低矮房屋模型进行框架结构设计并优化,加载风压荷载得到结构响应时程数据。然后,基于Hermite多项式模型(HPM)转换过程方法,估算得到响应的极值Ⅰ型分布(Gumbel);基于该极值估算方法,提出时程样本偏度、峰度、零超越次数与Gumbel分布两个参数之间的经验公式。接着,考虑前四阶矩的不确定性,利用经验公式以及多步概率分析,对任意分位点处响应极值的不确定性进行估计。最后,给出相关结论。     

A hierarchical Bayesian framework for calibrating micro-level models with macro-level data

Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level, at the district, urban, or national scale. This paper outlines a method for using macro-level data to calibrate micro-level models. A hierarchical framework is proposed, utilizing a combination of regression analysis and Bayesian inference. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types whilst quantifying uncertainty in both the empirical data and the generated energy estimates. Finally, the Bayesian regression method is validated and the use of the hierarchical Bayesian calibration framework is demonstrated.     

Wind engineering challenges of the new generation of super-tall buildings

The new generation of tall buildings is going much higher than before. This poses new challenges for wind engineering. The boundary layer models in many building codes and standards have served well for buildings less than about 300 m but more realistic models need to be used above 300 m. The statistics of upper level winds need also to be known with better certainty. New tools such as the archived global re-analysis data coming from weather forecast models can help shed more light on the upper level wind statistics. There are also questions to be answered about the effects on all tall buildings of non-synoptic wind profiles such as occur in thunderstorm downbursts and the Shamal winds of the Middle East. For the super-tall buildings wind tunnel testing is often commenced much earlier in the design than for lesser buildings. This permits the results to be used in a pro-active way to shape both the architectural design and structural design. The wind tunnel methods used include the force balance technique, aeroelastic modeling, high frequency pressure integration tests, as well as the traditional pressure model and pedestrian wind studies. A super-tall building pushes the limits of the force balance method due to difficulties in maintaining sufficient model stiffness and in accounting for the influence of higher modes of vibration. Since the impact of wind on people using terraces and balconies increases with building height, it is an issue needing particular attention for super-tall buildings.     

Effect of building integrated photovoltaics on microclimate of urban canopy layer

Building integrated photovoltaics (BIPV) has potential of becoming the mainstream of renewable energy in the urban environment. BIPV has significant influence on the thermal performance of building envelope and changes radiation energy balance by adding or replacing conventional building elements in urban areas. PTEBU model was developed to evaluate the effect of photovoltaic (PV) system on the microclimate of urban canopy layer. PTEBU model consists of four sub-models: PV thermal model, PV electrical performance model, building energy consumption model, and urban canyon energy budget model. PTEBU model is forced with temperature, wind speed, and solar radiation above the roof level and incorporates detailed data of PV system and urban canyon in Tianjin, China. The simulation results show that PV roof and PV façade with ventilated air gap significantly change the building surface temperature and sensible heat flux density, but the air temperature of urban canyon with PV module varies little compared with the urban canyon of no PV. The PV module also changes the magnitude and pattern of diurnal variation of the storage heat flux and the net radiation for the urban canyon with PV increase slightly. The increase in the PV conversion efficiency not only improves the PV power output, but also reduces the urban canyon air temperature.     

用于建筑渗风动态能耗分析的气象模型研究

建立了能耗统计的三维(气温、风速、风向)气象模型，并作了详尽的可行性分析计算，编制了通用性的气象统计模型的计算程序。研究结果可用于建筑供暖和渗风全年的动态能耗分析。     

Application of a Monte Carlo model to predict space heating energy use of Belgrade's housing stock

Detailed domestic stock energy models can be used to help formulate optimum energy reduction strategies. However, there will always be considerable uncertainty related to their predictions due to the complexity of the housing stock and the many assumptions required to implement the models. This paper presents a simple Monte Carlo (MC) model that can be easily extended and/or transformed in relation to data available for investigating and quantifying uncertainties in both the housing stock model's predictions and scenario assumptions. While 90% of the MC model predictions fell within a range which is ±19% the mean value, 50% of them were within ±8% of the mean. The findings suggest that the uncertainties associated with the model predictions and scenario assumptions need to be acknowledged fully and – where possible – quantified as even fairly small variability in the influential variables may result in rather large uncertainty in the aggregated model's prediction.     

分散式村镇建筑能效评估研究

针对中国城镇化进程中农村的变化,把村镇建筑分为分散式与集中式两种类型;总结了目前国内外建筑能效评估的两种方法,即建筑能耗基准评估和层次评估,在此基础上,结合分散式村镇建筑的特点,提出了适用于分散式村镇建筑的能效评估方法,通过建立统计基准模型对其进行评估;以沈阳某村镇的调研数据为样本,建立了该村镇多元回归统计模型,并以某户的建筑调研数据为例,给出其能效评估结果。     

光伏发电系统的不确定性分析

随着光伏发电的技术越来越成熟,成本逐渐降低,光伏发电系统作为主动式节能被大量的运用到建筑中。然而,由于建筑本身及其环境控制系统的复杂性,单凭设计师的经验和简单的计算方法无法确保预估的光伏发电系统能效的准确性。尤其是在设计前期以及后期系统运行和管理过程中,光伏发电提供能效的作用是否被夸大,其能效与实际情况是否一致,能否给予相对准确的预估都是研究的源起。利用建筑模拟作为研究手段,对光伏发电系统不确定性分析,将不确定性考虑到能耗之中,是建筑节能的关键之一。研究以目前发表的相关研究为基础,创新性地总结和定量了目前最流行和准确的"五参数模型"的不确定性,对现阶段建筑中利用光伏发电系统的能效评价具有实践性的参考作用。     

Uncertainty and sensitivity decomposition of building energy models

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters, especially when the modelling process is being performed before construction and commissioning. Past efforts to perform sensitivity and uncertainty analysis have focused on tens of parameters, while in this work, we increase the size of analysis by two orders of magnitude (by studying the influence of about 1000 parameters). We extend traditional sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics, which identifies which internal or intermediate processes transmit the most uncertainty to the final output. We present these results as a method that is applicable to many different modelling tools, and demonstrate its applicability on an example EnergyPlus model.