The creation of localized current and future weather for the built environment

Lead Research Organisation: University of Bath
Department Name: Architecture and Civil Engineering

Abstract

It is well known that climate change will have a significant impact on UK building design and energy use. It is also known within the building science and architectural communities that the current weather files used for thermal modeling of buildings only represent average weather rather than heat waves or cold snaps. As was shown by the 14,000 deaths in Paris during the 2003 heat wave, this is a highly serious issue and there is the need to ensure future buildings are designed to deal with future weather, or extremes of current weather.
In addition, the current weather files used by the construction industry and building scientists divide the UK into only 14 regions, with, for example, the whole of the South West peninsular (including up-land areas) being assigned the coastal Plymouth weather file. It is known that this can easily lead to a 200% error in the estimation of annual energy demand. The scale of this error is such that it renders many of the dynamic simulations carried out by engineers questionable. This is unfortunate when simulation is used within the framework of the building regulations, but it is fatal when trying to use simulation to estimate how resilient a pre-existing building is, or the danger its vulnerable occupants might be in.
The aim of this project will be to see if a method can be devised that is capable of creating local weather from 2015 to 2080 covering the whole UK at a resolution of 5km, and to include within this files that represent various excursions from the mean: e.g. heat waves and cold snaps.
An interdisciplinary approach is envisaged with the project separated into six work packages:
WP1 We will use a method already published by the team together with the UKCP09 weather generator to produce current and future typical weather at a resolution of approximately 5km.
WP2 The work in the previous work package will initially require the creation of thousands of years of weather per site. Within these initial years will reside a large number of weather events of interest to the building scientist or engineer. These files will be used in computer models of 1200 differing architectures and building uses to identify what are the key drivers of weather variable coincidence that defines the likelihood of building system failure or thermal issues for occupants.
WP3 Having characterised which events best describe the stresses on a building, its occupants and systems in WP2. Event years (i.e. times series of weather data variables on a one hour time step that represent atypical hot, dry, cold and wet periods) will be created for the whole UK.
WP4 Having generated the event years, and simulations from the 1200 buildings, the two will be recombined to produce the first map of UK resilience to a changing climate. Although others have looked at the regional resilience of the built environment using average weather years, the concern is not about the response of building and occupants to such average time series, but to more extreme events.
WP5 Given the large number of files proposed, guidance will need to be given on which to use in practice, and how this might be expressed in the building regulations and other documentation.
We plan to use case studies as the main guidance tool. This will add greatly to their intellectual validity within the target audience of practicing engineers. In total, we expect the guidance to be tested on >100 real building projects.
WP6 Impact. All weather files produced by the project will be publicly available for a minimum of 10 years. A series of road shows will be undertaken at the end of the project. At these events the results of the project will be presented to a large number of users. The idea will be to introduce the whole UK built environment community to the idea of designing resilient buildings aided by the weather data produced by the project. A short film will also be produced for those that cannot attend and for an international audience.

Planned Impact

It is well known that climate change will have a significant impact on UK building design and energy use. The predicted temperature changes are large enough to ensure that some buildings will either become uncomfortable places to be or will fail parts of the building regulations. This will have an impact on human health, particularly for the elderly in high summer.

Although this proposal concentrates on the construction industry and building science, the use of weather files in research and design is far more widespread-agricultural science being an obvious example-and the methodology and results produced will be of importance to a wide audience.

Much of the work described is urgently needed. The use of a single set of test reference years in the bulk of design work in the UK has led to a position where for all but the highest profile buildings little effort is made to explore what the internal environment of a building is truly likely to be over a range of typical years, and simulation is seen only as a tool to meet the building regulations. Simultaneously, we know that little attempt has been made to future-proof current designs against a changing climate. This has serious consequences for energy costs, productivity in the work place, competitiveness and health (particularly of the elderly).

The UK is committed to substantial reductions in its carbon emissions and the Government has highlighted the buildings sector as one of the major sectors to help ensure these reductions. Without knowledge of how energy consumption is likely to change in a changing climate it is difficult to make predictions of future carbon emissions. For buildings, unlike say transport, future energy consumption is intrinsically linked to future climate. The demand for heating or cooling cannot be estimated in a rigorous way by analysts unless they have access to consistent predictions of climate in the form used by energy/thermal models of buildings: i.e. hourly weather data that include wind direction.

Through its major architectural and engineering practices, the UK has a world-wide reputation in building design and modelling. The inability to model a changing climate is a weakness in this sector which leaves it open to competition from overseas. If the UK had an industry-wide ability to model these effects in the buildings sector based on a well-researched and consistent approach it would undoubtedly lead the way for global reform of the sector, giving it a competitive advantage.
Building professionals as represented by the membership of CIBSE, RIBA and other organisations have as yet little experience of using probabilistic information. To remain competitive this needs to change. Part of the work proposed is an in-depth exploration of the challenges we face in presenting the concepts of probabilistic data and results within an industrial setting, and the production of case-studies relevant to industry. Because this element is so industry-centred, many of the results will be applicable to other related sectors.

Publications

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Coley D (2017) Probabilistic adaptive thermal comfort for resilient design in Building and Environment

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Herrera M (2017) A review of current and future weather data for building simulation in Building Services Engineering Research and Technology

 
Description We seem to have found that there is less point than previously assumed in using realistic weather and that mathematical functions do almost as good a job
Exploitation Route By developing a whole new field of simulation
Sectors Construction

 
Description Yes we have create a website that the public can access to download future weather
First Year Of Impact 2019
Sector Construction
Impact Types Economic

 
Title Dataset for "Mitigation versus adaptation: Does insulating buildings increase overheating risk?" 
Description Dataset for journal article "Mitigation versus adaptation: Does insulating buildings increase overheating risk?". The dataset contains the summary simulation results of the building simulation parametric study (EnergyPlus v8.9) for overheating, natural ventilation and space heating demand (annual simulations with yearly indicators). The dataset contains the performance of all the buildings that combine the following parameters: dwelling types, insulation levels, thermal mass, window sizes, shading strategies, internal gains, window opening rubrics, algorithms, infiltration levels, building orientations and locations. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Title Dataset for Future probabilistic hot summer years for overheating risk assessments 
Description Future hot summer years to be used for assessing risk of overheating and heat stress under a changing climate. They were created in two alternative ways: one is based on Weighted Cooling Degree Hours, the other is based on Physiologically Equivalent Temperature. 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes  
 
Title Probabilistic adaptive thermal comfort for resilient design 
Description Adaptive thermal comfort theory has become the bedrock of much thinking about how to judge if a free-running environment is suitable for human occupation. In design work, the conditions predicted by a thermal model, when the model is presented with one possible annual weather time series (a reference year), are compared to the limits of human comfort. If the temperatures are within the comfort limits, the building is judged to be suitable. However, the weather in many locations can vary year-on-year by a considerable margin, and this begs the question, how robust are the predictions of adaptive comfort theory likely to be over the many years a building might be in use? We answer this question using weather data recorded for up to 30 years for locations within each of the five major Köppen climate classifications. We find that the variation in the annual time series is so great that the predicted comfort temperature frequently lies outside the acceptable range given by the reference year. Return periods for the excursions of the time series are calculated for each location. The results for one location are then validated using the world's longest temperature record. These results suggest that industry and academia would be best advised to move to a probabilistic methodology, like the proposed one, when using adaptive comfort theory to judge the likely conditions within a building. Extra pertinence is provided by concerns over increases in mortality and morbidity in buildings due to a rapidly warming climate. 
Type Of Material Database/Collection of data 
Year Produced 2017 
Provided To Others? Yes  
 
Title Quantile Regression Ensemble Summer Year (QRESY) 
Description The zip file contain 4 datasets in csv format. Each of them correspond to weather files of one hot summer year hourly data based on the weather observed over 40 (basis) years, 1974 - 2013. Two are the so-called probabilistic design summer years (PDSY) for the cities of London (UK) and Joao Pessoa (Brazil). The PDSY uses an overheating metric that is based on the number of hours in which the temperature is above a certain threshold when a building is occupied. Then, PDSY is created by selecting an entire year which contains the third hottest mean based on this overheating metric. PDSY is currently used in the UK as reference of warm summers. However it is the first time that a PDSY is created for Brazil. The other two weather files correspond to the new quantile ensemble regression summer year (QRESY) also aiming to represent hot summers both for London and Joao Pessoa. QRESY is created by combining observed summer extreme temperatures. This is done by endowing higher weights to quantiles away from the median for ensembles within upper quantiles. At the same time, it increases the importance of quantiles near to the median for combining lower quantiles. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
 
Title The building performance gap: Are modellers literate? 
Description Excel file containing the survey results and analysis for the paper of the same name. 
Type Of Material Database/Collection of data 
Year Produced 2016 
Provided To Others? Yes