The appropriate spatial resolution of future weather files for building simulation

Building thermal modelling packages require weather data to predict representative internal conditions. Typically, around the world, reference weather years of various forms are used which are created from observations at aparticular location. However, it is unlikely that this location is identical to that of the building. This can lead toweather files for coastal locations being applied to inland and upland sites or vice versa. In the UK, the UKCP09 weather generator has the ability to produce weather at a 5 km resolution. Currently, it is unclear how useful this extra spatial resolution will be and it is this question that is addressed here. It is found that for both future and present climate, the spatial variability of the weather is the dominating factor. Although there are geographies where a low spatial resolution can be used, there are regions where a much higher resolution is necessary.     

Thermal building simulation using the UKCP09 probabilistic climate projections

This article investigates how to use UK probabilistic climate-change projections (UKCP09) in rigorous building energy analysis. Two office buildings (deep plan and shallow plan) are used as case studies to demonstrate the application of UKCP09. Three different methods for reducing the computational demands are explored: statistical reduction (Finkelstein-Schafer [F-S] statistics), simplification using degree-day theory and the use of metamodels. The first method, which is based on an established technique, can be used as reference because it provides the most accurate information. However, it is necessary to automatically choose weather files based on F-S statistic by using computer programming language because thousands of weather files created from UKCP09 weather generator need to be processed. A combination of the second (degree-day theory) and third method (metamodels) requires only a relatively small number of simulation runs, but still provides valuable information to further implement the uncertainty and sensitivity analyses. The article also demonstrates how grid computing can be used to speed up the calculation for many independent EnergyPlus models by harnessing the processing power of idle desktop computers.     

Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study

This study explores the uncertainties and sensitivities in the prediction of the thermal performance of buildings under climate change. This type of analysis is key to the assessment of the adaptability and resilience of buildings to changing climate conditions. The paper presents a comprehensive overview of the key methodological steps needed for a probabilistic prediction of building performance in the long term future (50 to 80 years). The approach propagates uncertainties in climate change predictions as well as the uncertainties related to interventions in building fabric and systems.A case study focussing on an air-conditioned university building at the campus of the authors is presented in order to demonstrate the methodology. This employs the most recent probabilistic climate change projections for the United Kingdom (UKCP09 dataset) and takes into account facility management uncertainties when exploring uncertainties in the prediction of heating energy, cooling energy, and carbon emissions.     

Towards risk‐based water resources planning in England and Wales under a changing climate

The publication of the UKCP09 climate change projections for the United Kingdom provides the opportunity for more rigorous inclusion of climate change uncertainty in water resources planning. We set out how the current approach to incorporating climate change and other uncertainties in water resources planning may be updated to incorporate the UKCP09 projections. In an uncertain future, the frequency with which customers will experience water shortages cannot be predicted for sure, so a water company cannot predict definitely whether it will or will not fulfil its Level of Service commitments. We therefore go on to propose that the probability of failing to meet Level of Service (for given populations of customers) provides an appropriate metric of risk, which conveniently summarises the uncertainties associated with supply and demand, including climate change uncertainties. We sketch out how this risk metric can be calculated based upon simulation modelling of the water resource system.     

Climate change future proofing of buildings—Generation and assessment of building simulation weather files

Simulation packages for predicting building performance in terms of energy and comfort are becoming increasingly important in the planning process. However, current industry standard weather files for building simulation are not suited to the assessment of the potential impacts of a changing climate, in particular summer overheating risks. In addition, no bespoke climate change weather files are readily available that can be loaded directly into environmental simulation software. This paper describes the integration of future UK climate scenarios into the widely used Typical Meteorological Year (TMY2) and EnergyPlus/ESP-r Weather (EPW) file formats and demonstrates the importance of climate change analysis through a case study example. The ‘morphing’ methodology published by the Chartered Institution of Building Services Engineers (CIBSE) is utilised as a baseline for transforming current CIBSE Test Reference Years (TRY) and Design Summer Years (DSY) into climate change weather years. A tool is presented that allows generation of TMY2/EPW files from this ‘morphed’ data and addresses the requirements related to solar irradiation, temperature, humidity and daylighting beyond the parameters provided by CIBSE weather years. Simulations of a case study building highlight the potential impact of climate change on future summer overheating hours inside naturally ventilated buildings.     

Analysis of probabilistic climate projections: heat wave,overheating and adaptation

Climate change could substantially impact on the performance of buildings in providing thermal comfort to occupants. The recently launched UK climate projections (UKCP09) suggest that all areas of the UK will become warmer in the future with the possibility of more frequent and severe extreme events, such as heat waves. This study, as part of the low carbon futures (LCF) project, explores the consequent risk of overheating and the vulnerability of a building to extreme events. A simple statistical model proposed by the LCF project elsewhere has been employed to emulate the outputs of the dynamic building simulator (ESP-r), which if directly used with the numerous replicated climates available from a probabilistic climate database could be practically challenging. For complex probabilistic climate datasets, we demonstrate the efficiency of the statistical tool in performing a systematic analysis of various aspects of heat waves including: frequency of extreme heat events in changing climate; its impact on overheating issues and effects of specific adaptation techniques applied to offset predicted overheating. We consider a domestic building as a virtual case study. Results are presented relative to a baseline climate (1961–1990) for three future timelines (2030s, 2050s and 2080s) and three emission scenarios (Low, Medium and High).     

An adaptive methodology for risk classification of small homogeneous earthfill embankment dams integrating climate change projections

This paper presents the application of the advanced probabilistic slope stability model with precipitation effects developed to assess the performance of small homogeneous earthfill embankment dam slopes, when exposed to future seasonal precipitation scenarios. Here, the UK's latest probabilistic climate model known as UKCP09 is applied. To reflect the critical conditions conducive to slope failure, a benchmark has been developed to identify the change, if any, in the risk classification of the slope's performance level due to precipitation. Thus, enabling the reassessment of the dam's risk classification, as categorised by the Flood and Water Management Act 2010. Such an approach could therefore be well placed to support and enhance the decision-making process, its impact on the public, especially in relation to future climate effects.     

The sensitivity of fluvial flood risk in Irish catchments to the range of IPCC AR4 climate change scenarios

In the face of increased flood risk responsible authorities have set out safety margins to incorporate climate change impacts in building robust flood infrastructure. Using the case study of four catchments in Ireland, this study subjects such design allowances to a sensitivity analysis of the uncertainty inherent in estimates of future flood risk. Uncertainty in flood quantiles is quantified using regionalised climate scenarios derived from a large number of GCMs (17), forced with three SRES emissions scenarios. In terms of hydrological response uncertainty within and between hydrological models is assessed using the GLUE framework. Regionalisation is achieved using a change factor method to infer changes in the parameters of a weather generator using monthly output from the GCMs, while flood frequency analysis is conducted using the method of probability weighted moments to fit the Generalised Extreme Value distribution to ~ 20,000 annual maximia series. Sensitivity results show that for low frequency events, the risk of exceedence of design allowances is greater than for more frequent events, with considerable implications for critical infrastructure. Peak flows for the five return periods assessed were found to be less sensitive to temperature and subsequently to potential evaporation (PET) than to rainfall. The average width of the uncertainty range for changes in flood magnitude is greater for low frequency events than for high frequency events. In all catchments there is a progressive increase in the peak flows associated with the 5, 25, 50 and 100-year return periods when moving from the 2020s to the 2080s.     

A computational method to assess the impact of urban climate on buildings using modeled climatic data

In the last decade a lot of research effort has been spent on identifying the effect of the urban climate on the energy performance of buildings and HVAC (heating ventilation and air conditioning) systems. In this paper a methodology is proposed to assess that effect by the exclusive use of modeled data. Particularly, by using a downscaling modeling procedure a process for the generation of weather files for energy calculations is achieved. From a non-hydrostatic weather model the urban climate of the city of Lisbon is simulated and via a weather generator climatic data files for the buildings’ simulation software are derived. The results on the energy behavior of a reference test cell for different sites inside the city of Lisbon are presented.     

Sol-air temperature and daylight illuminance profiles for the UKCP09 data sets

To provide information on climate change, the UK Climate Impacts Programme (UKCIP) provided the latest UKCP09 data to a resolution of 5 km square grids for the UK. Those data sets were used in this study along with the historical measured data for two locations—Bracknell (London) and Edinburgh—to critically analyse the likely changes that may occur in the sol-air temperature and daylight illuminance profiles. These parameters have an important bearing on the design and function of buildings and building services.     

Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong

The concern on climate change leads to growing demand for minimization of energy use. As building is one of the largest energy consuming sectors, it is essential to study the impact of climate change on building energy performance. In this regard, building energy simulation software is a useful tool. A set of appropriate typical weather files is one of the key factors towards successful building energy simulation. This paper reports the work of developing a set of weather data files for subtropical Hong Kong, taking into the effect of future climate change. Projected monthly mean climate changes from a selected General Circulation Model for three future periods under two emission scenarios were integrated into an existing typical meteorological year weather file by a morphing method. Through this work, six sets of future weather files for subtropical Hong Kong were produced. A typical office building and a residential flat were modeled using building simulation program EnergyPlus. Hourly building energy simulations were carried out. The simulated results indicate that there will be substantial increase in A/C energy consumption under the impact of future climate change, ranging from 2.6% to 14.3% and from 3.7% to 24% for office building and residential flat, respectively.     

A frost decay exposure index for porous,mineral building materials

The disintegrative process of freezing and thawing of porous, mineral materials represents a significant challenge in the design and construction of building enclosures. In this paper, we present a simple method for assessing the relative potential of a climate to accelerate frost decay based on multi-year records of daily air temperatures and rainfall, with special emphasis on masonry. Distributions of 4-day rainfall prior to days with freezing events provide quantitative information on the geographically dependant frost decay risk in porous, mineral building materials in a given climate. Data from 168 weather stations in Norway are analysed, using weather data from the reference 30-year period 1961–1990.     

Preparation of future weather data to study the impact of climate change on buildings

The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables. In order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since most of building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. In this paper, the methods used to prepare future weather data for the study of the impact of climate change are reviewed. The advantages and disadvantages of each method are discussed. The inherent relationship between these methods is also illustrated. Based on these discussions and the analysis of Australian historic climatic data, an effective framework and procedure to generate future hourly weather data is presented. It is shown that this method is not only able to deal with different levels of available information regarding the climate change, but also can retain the key characters of a “typical” year weather data for a desired period.     

Evacuation models are running out of time

The representation of crowd movement in existing evacuation models is typically based on data collected in the 1950s to 1980s, i.e., data that are more than 40 years old. Since the 1970s, population characteristics have changed dramatically around the world. Reports show that the percentage of elderly and obesity rates have increased significantly and this trend is predicted to continue into the future. Recent research [1], [2], [3] illustrates the magnitude by which different age cohorts of a population group can reduce the general speed and flow rates. In addition, well established studies have quantified the impact of body dimensions on speed and flow [4]. However, many existing evacuation models fail to take the changing characteristics of populations into account. This paper aims to review existing knowledge of population demographics and crowd dynamics, derive an indicative flow reduction factor for future populations, and consider the implications for computer models and building design in the future.     

Will future low-carbon schools in the UK have an overheating problem?

Meeting thermal comfort and internal air quality standards for schools can be difficult for buildings that, traditionally in the UK, have not used mechanical ventilation and air-conditioning. With a trend towards increased internal gains, and climate change predicted to cause a significant rise in temperatures, this issue becomes more problematic. Considering this within the context of low-carbon buildings creates an added hurdle—can low-carbon schools be produced that will provide a comfortable teaching environment in the future? Through a series of simulations on template school buildings, this study highlights the effect that future small power and lighting energy use could have on reducing the overheating of school teaching areas. The effect of a warming climate is also estimated, and the impact that has on the internal temperatures of a school quantified. Introducing external shading and increasing ventilation in classrooms can reduce overheating significantly but, for many cases, the risk that the school building cannot cope with the overheating problem might still remain.     

Resilience of naturally ventilated buildings to climate change: Advanced natural ventilation and hospital wards

Naturally ventilated buildings have a key role to play mitigating climate change. The predicted indoor temperatures in spaces with simple single-sided natural ventilation (SNV) are compared with those in spaces conditioned using a form of edge in, edge out advanced natural ventilation (ANV) for various UK locations. A criterion, for use in conjunction with the BSEN15251 adaptive thermal comfort method, is proposed for determining when the risk of overheating, both now and in the future, might be deemed unacceptable. The work is presented in the context building new, and refurbishing existing, healthcare buildings and in particular hospital wards. The spaces conditioned using the ANV strategy were much more resilient to increases in both internal heat gains and climatic warming than spaces with SNV. The ANV strategy used less energy, and emitted less CO₂ than conventional, mechanically ventilated (MV) alternatives. In a warming world, the ‘life-expectancy’ of passively cooled buildings can be substantially influenced by the internal heat gains. Therefore, resilience to climate change, susceptibility to internal heat gains and the impact of future heat waves, should be an integral part of any new building or building refurbishment design process.     

Managing and interpreting uncertainty for climate change risk

Climate change is a significant risk for the built environment because it implies not only warmer weather, but also more extreme weather events, such as storms, droughts and heat waves. Considerable uncertainty about the future also exists, partly because of the response of society's apparent reluctance to mitigate climate change by reducing fossil fuel consumption. An adaptive response to the challenge draws on the literature on climate change, the urban environment, natural hazards and risk analysis. Two concepts - life cycle costs and the avoidance of ruin - provide a useful framework for factoring the uncertainty associated with climate change into a risk analysis for the built environment. Monitoring, prediction, data management and communication are the unglamorous underpinnings of a successful urban risk-management strategy. For cities to develop a significantly improved response capacity, the active support of senior levels of government is essential because cities have neither the legal powers nor the resources to tackle climate change on their own. Ultimately, the biggest challenges are institutional and behavioural.     

气候变暖对建筑能耗的影响

能源消耗与气候具有密切关系。一方面能源的开发利用会对气候产生影响；另一方面气候变化又直接影响到能源消耗。建筑部门就是受此影响部门之一。供暖空调能耗在建筑能耗中所占比重较大。通过对我国若干城市近几年的1月平均温度、冬季平均温度与常年值进行比较，利用度日数等气候指标分析了气候变暖对建筑物供暖能耗的影响。同时探讨了气候异常时建筑物空调能耗的变动状况。     

Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates

As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.     

Longitudinal prediction of the operational energy use of buildings

Thus far most studies of operational energy use of buildings fail to take a longitudinal view, or in other words, do not take into account how operational energy use changes during the lifetime of a building. However, such a view is important when predicting the impact of climate change, or for long term energy accounting purposes. This article presents an approach to deliver a longitudinal prediction of operational energy use. The work is based on the review of deterioration in thermal performance, building maintenance effects, and future climate change. The key issues are to estimate the service life expectancy and thermal performance degradation of building components while building maintenance and changing weather conditions are considered at the same time. Two examples are presented to demonstrate the application of the deterministic and stochastic approaches, respectively. The work concludes that longitudinal prediction of operational energy use is feasible, but the prediction will depend largely on the availability of extensive and reliable monitoring data. This premise is not met in most current buildings.