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Article

Communicating futureoverheating risks to buildingdesign practitioners: Using theLow Carbon Futures tool

Mehreen Saleem Gul1, DP Jenkins1, S Patidar1,GF Menzies1, PFG Banfill1 and GJ Gibson2

Abstract

The Low Carbon Futures tool provides a probabilistic assessment of future overheating risks and cooling

demands for domestic and nondomestic buildings in the UK. The approach adopted for the development

of the Low Carbon Futures tool includes academic rigour within the development of the calculation

engine, and also practitioner feedback throughout the process. This paper discusses the journey of the

tool from modelling and simulation to the practitioner engagement, which took place by means of a

questionnaire, focus groups and interviews with building design professionals aimed at understanding how

the issue of overheating in buildings is being addressed. Throughout these events, the synergies between

designing for low-carbon targets and designing for a future climate were explored. A final dissemination

event was held to identify output styles that could be generated by the Low Carbon Futures tool that

would be more practical and useful for specific client types. The workshop discussions serve to shape the

outputs from the tool, and the feedback gathered will be used to inform a number of output styles, based

on client type.

Practical application: This paper outlines the development of the Low Carbon Futures tool for analysing

overheating risks in buildings and discusses the practitioner feedback obtained from industry professionals

on the use and applicability of the tool, in a final event hosted by the Low Carbon Futures research team in

London. This event confirmed that practitioners need to be comfortable with the layout and format of the

output in order to communicate its meaning and possible implications to a range of clients. A balanced

output is required, which conveys some of the complexity of the underlying analysis, but which is easily

understood and conveyed to a potentially lay audience.

Keywords

Overheating risk, buildings, practitioners, Low Carbon

1Urban Energy Research Group, School of Built Environment,

Heriot-Watt University, Edinburgh, UK

2Maxwell Institute for Mathematical Sciences, School of

Mathematical and Computer Sciences, Heriot-Watt University,

Edinburgh, UK

Corresponding author:

Mehreen Saleem Gul, Heriot-Watt University, Riccarton

campus, Edinburgh EH14 4AS, UK.

Email: [email protected]

Building Serv. Eng. Res. Technol.

2015, Vol. 36(2) 182–195

! The Chartered Institution of Building

Services Engineers 2015

DOI: 10.1177/0143624414566475

bse.sagepub.com

at Heriot - Watt University on June 8, 2015bse.sagepub.comDownloaded from

http://bse.sagepub.com/

Introduction

The effects of changing climate in the UK areleading towards potentially hotter, drier sum-mers and milder, wetter winters.1 The significantrisk of increases in mean temperature by the endof this century might mean uncomfortable highinternal temperatures for building occupants.Some modern highly insulated buildings arealready experiencing uncomfortably high inter-nal temperatures, even in fairly moderate sum-mers. This demands us to adapt the way inwhich we design, construct and use our build-ings to combat the anticipated changes.Excessive heat gains from both internal andexternal sources alongside inappropriate or inef-fective ventilation strategies are amongst thefundamental concerns that must be understoodand addressed by designers.2 Mechanical venti-lation is an energy-intensive process, andair-conditioning is even more so. The majorchallenge faced by the building industry is,therefore, to design energy-efficient buildingsthat not only provide thermal comfort andacceptable indoor air quality but also consumeless energy.

Significant efforts are required to informbuilding design processes to best adapt to chan-ging climates. Any message that warns of thedangers of climate change should also clearlyprovide solutions – solutions that are both effect-ive and possible for industry to perform or sup-port.3 The 2010 release of the UK probabilisticclimate data projections (UKCP09) by UKClimate Impacts Programme (UKCIP) providesin-depth information to understand the risksposed by an uncertain future.4 The Low CarbonFutures (LCF) project has developed a tool tointegrate these complex probabilistic projectionsto understand potential overheating risks inbuildings. The academic rigour of the LCF toolindicates that this will help design teams makeearly provisions (adaptation) to avoid thesefuture risks.5,6 However, this raises the questionof whether practitioners think the same? Oftenthe best research ideas are poorly communicatedto industry practitioners and thus they cannot bemeaningfully adopted and used to affect change.

Throughout the development of the LCF tool,feedback was sought from building professionalsto tailor and steer the format and outputs of thetool to increase relevance and practicality.7,8 Thistwo-way communication also served as amediumto bridge the gap between research and practice,by raising awareness of future overheating risksin buildings.

This paper presents the journey of four yearsof intensive research of the LCF tool develop-ment and sums up the modelling and simulationprocess as well as the practitioner engagementprocess. In particular this paper talks about aspecific dissemination and discussion workshopthat was held with a targeted audience, to betterunderstand how the LCF tool can be adaptedand used in industry, particularly for overheat-ing analyses. The workshop discussions serve toinform how further investigation should bemade into expanding the use of the tool and toshape a number of output styles, based on clienttype.

The LCF tool development

At a glance, the four-year journey (2009–2013)of the LCF tool development can be seen inFigure 1. The modelling and simulation processinvolved producing a calculation engine, whichprovides significant simplification to the com-plex building and climate information in deter-mining the risk of a specific building overheatingin the future.5,6 The practitioner engagementprocess involved interaction with representativesfrom the building industry to assess attitudes,opinions and preferences and to tease outthe issues of designing buildings for potentialfuture summer overheating risks.7,8 How thetwo processes are linked and how it led tothe evolution of the LCF tool is explained inthe sections given below.

Modelling and simulation

This section presents some brief accounts on themodelling and simulation process involved inthe development of the LCF tool.

Gul et al. 183



Statistical analysis of climate files. The LCFproject proposed to explore the potential of thelatest climate information developed by the UKClimate Projection (UKCP09) programme withdynamic building simulation, for conductingdetailed overheating risk analyses of the UKbuilt environment in a future climate. The firststep in the development of the LCF toolinvolved the processing of the climate informa-tion effectively for use with the dynamic

simulation modelling (DSM), processed consist-ently with the different climate scenarios. TheLCF study employed ESP-r9 as a building simu-lation engine, which requires annual climate filesat an hourly resolution. For each user-specifiedclimate scenario, the ‘Weather Generator’ toolavailable from UKCP094 can generate up to 100time series of 30 climate years at an hourly reso-lution. To create these 100 time series, theWeather Generator samples 100 points

2009

Practitioner Engagement Process

Modelling and Simulation Process

Timeline

Questionnaire

FG 1

Final event

FG 3 and 4

Interviews 2

Statistical Analysis of climate files

Building Simulation &

Statistical Modelling

Prototype

Final tool

Successful Emulation

2010

2011

2012

2013

FG 2

Interviews 1

Outputs

Further Work

Figure 1. The journey of the LCF tool.

184 Journal of Building Services Engineering Research & Technology 36(2)



consistently from the underlying probability dis-tribution of the specified scenarios and exploitsthe statistical characteristics of the historicaldata to generate these 30-year time series. Witheach of the 30 climate files within each of thesetime series having similar statistical properties,the LCF method applied a random samplingalgorithm to select a single climate year ran-domly from each of these 100 time series tocreate a sample of 100 climate files for a selectedscenario, which retains similar probabilisticaspects and statistical properties of the entiredata set of 3000 climates years.

Detailed information on the random samplingalgorithm method used in the development ofLCFtoolhasbeendescribedelsewhere,5 includinga further justification for applying this method.

Building simulation. The probabilistic climateinformation is processed specifically for usewith DSM (where the project carried out simu-lations10 using the aforementioned ESP-r butalso the commercial software IES-VE(Integrated Environmental Solutions–VirtualEnvironment).11 The result is an output thatpreserves the hourly resolution of the simula-tion, but also retains the probabilistic descrip-tion of the input climate data. However, theregression equation (Regression and Validation(Successful Emulation) section) that producesthis probabilistic output requires an initialdynamic simulation of a building to calibratethe regression coefficients. A user of the toolwould therefore carry out a building simulationfor a specific design under any test weather file,much like they would for any normal buildingproject to estimate heating–cooling loads. Thetool allows the user to select what type ofmetric they are interested in and, therefore,what metric the regression tool will be appliedto (e.g. internal temperatures, heating or coolingloads). The focus could be an individual zone orthe building as a whole, using room-averagedoutput metrics. With the output metric chosen,the hourly output file is paired with the hourlyclimate file (used for that simulation) and pre-pared for the regression tool itself.

The use of DSM, while posing a challenge formultiple-climate simulations, is crucial for mostenergy analyses of more complex buildings, butparticularly for overheating. Understandinghow a building responds over time to transientparameters (including internal heat sources aswell as weather) is important when, for example,looking at the number of hours that mightexceed a temperature threshold within the build-ing. The LCF tool is thus only applied for DSMstudies.

Regression and validation (successfulemulation). In practice, DSMs are designed(particularly within commercial practice) tosimulate one weather file per simulation, andsimulating 100 s of UKCP09 probabilistic cli-mate files is unlikely to be practical for a typicalbuilding project. This challenge motivates thedevelopment of the LCF tool, which involvedan intensive analysis of the probabilistic climateprojections by means of statistical data reduc-tion techniques such as principal componentanalysis (PCA). PCA has been used to signifi-cantly reduce the dimension of weather variablesthat are required to calibrate a robust regressionmodel for interfacing climate information withthe DSM simulation outputs. To establish sucha robust regression-based model, 72 h of historichourly values corresponding to seven differentweather variables (i.e. 72� 7¼ 504) need to beregressed against hourly DSM outputs, such asinternal temperature–heating–cooling loadvalues. However, the PCA technique used inthe modelling process effectively reduces these504 weather variables into a set of 33 compo-nents after retaining 95% of the total variabilityof the climate information. This makes theregression process much more efficient andallows even an ordinary desktop computer tocarry out the calculation within a reasonabletimescale (e.g. the regression model can producethe equivalent of 400 building simulations withinan hour).

As discussed, the regression model requiresjust one DSM simulation for calibration andhas been vigorously validated for a range of

Gul et al. 185



case study buildings and adaptation scen-arios.5,6,8 Once calibrated, the regressionmodel can be used to process hundreds of prob-abilistic climate within minutes to emulatecorresponding DSM outputs and, more gener-ally, encapsulates how that particular buildingwill respond to climate (such that it would notrequire resimulation for any change in climatescenario or location). More details on the stat-istical methods described here can be foundelsewhere.6

Outputs. While the regression tool abbreviatesthe required simulation process, it still produceshundreds of hourly output files that require col-lation into something useable. Without thisstage, the tool would not have any practicalusage for a real building project. As describedin Practitioner Engagement section, practitionerengagement was critical to this process. The finaltool uses two types of output formats(see Preferred choices section Figure 3(a) and(b)) for different users. The first output, a prob-abilistic graph, emphasises the spectrum ofprobabilities that the tool generates and mightbe used for a more detailed exploration of how abuilding might perform under different futureclimate conditions. This would usually onlybe appropriate for someone with buildingmodelling experience, but might be suitable fora cost–benefit analysis (e.g. deciding whether agiven level of failure risk is worth achieving at agiven capital cost for specific adaptation meas-ures for that building).

The second output format is a simpler,colour-coded risk matrix that demonstrateshow, for example, an overheating risk mightchange for future climates as well as chosenadaptations. This would be tailored towards asingle failure threshold (e.g. risk that the internaltemperature exceeds 28�C for more than 1% ofoccupied hours) and, due to this increased clar-ity, might be more suitable for a client of abuilding project.

For more involved analysis, the individualfiles generated by the regression tool wouldalso be available to any user of the model; that

is, the resulting output files for every single cli-mate file run through the tool for that building.This would be superfluous for many users, butallows for error checking and more detailedbuilding failure identification.

Prototype. Though the engine of the LCF toolis based on complex regression techniques, ithas been designed to integrate practitionerfeedback (collected at different stages of theproject) to develop a proficient prototype,which can be easily used by building profes-sionals. The prototype LCF tool relies on justbasic information from the user (including oneDSM output file) to generate a range of prob-abilistic outputs indicating how a buildingmight perform in a future climate. In additionto the climate file and building simulation file(for calibration), the user is also asked for thefollowing input, all of which would already beknown for any building simulation exercise.While ESP-r is referenced, an identical processwould be carried out for other dynamic simu-lation software):

1. Number of zones used in ESP-r simulation.2. Floor area of each zone.3. Year and day type used for ESP-r simulation.4. Occupancy profile specifying occupied and

unoccupied hours during a typical weekdayand weekend.

5. Internal heat gain and air change profiles forweekday and weekends.

6. Total number of probabilistic climate tosimulate with regression tool.

7. Threshold values for overheating analysis.Similarly, threshold for cooling and heatingplant risk analysis. Any definition of failurecan be used providing it can be calculatedfrom hourly building simulation output(e.g. a definition of overheating wouldnot be restricted by the LCF tool, it onlyneeds to be calculable from dynamicsimulation).

8. Geographic location and future climate scen-ario for overheating risk analysis.




9. Bin size for generating probabilistic–cumula-tive distribution function and plots (with adefault value provided).

10. One ESPr simulation output file to calibrateregression coefficients.

The final tool. The user interface of the finaltool is still in development, though the formsof output have been finalised (based onPractitioner Engagement section). Also, whilefurther validation would allow the tool to betested for a wider range of scenarios, buildingtypes and software (i.e. tested for emulation ofsoftware beyond just ESP-r and IES-VE), thebasic calculation engine is robust for the statedapplications. Further changes would be focussedon improving the efficiency even further; thiscould be achieved by directly integrating thetool into a specific building package, so thatthe regression tool could run automatically fol-lowing a single building simulation.

Practitioner engagement. The research instru-ments for this process consisted of a preliminaryonline questionnaire, focus groups (FGs) andinterviews aimed at getting insights into where

the industry stands in relation to adapting forclimate change and how to best apply the LCFproject research.

Questionnaire. The practitioner engagementprocess began once the validation exercise(Regression and Validation (SuccessfulEmulation) section) confirmed that a buildingsimulation based on one climate can be ade-quately represented for multiple climatesthrough a regression approach. An online pre-liminary questionnaire was distributed at thisstage to building design professionals to get afeel for the current building design proceduresas well as how typical practice differs from bestpractice.7 The survey link was provided throughone of the fortnightly Chartered Institution ofBuilding Services Engineering newsletter wherea total number of 43 people responded to thissurvey. The results indicated that the majority ofthe respondents do not employ any practicaltechniques to increase the resilience of buildingsthey design in order to overcome future over-heating risks. Figure 2 depicts a summary ofthe difference between typical and best practicewhere the overlapped area shows parametersthat are always considered. Based on the

Standards & Regulations

Energy targets Clients-

requirements Internal Air-

Quality

Typical Practice

Capital Costs Available Budget Air Conditioning Room Air - Temperature Occupancy Details Services Availability

Best Practice

Running Costs Building Orientation Solar Shading Occupant Comfort Climate Data Use Overheating - Assessment

Figure 2. Typical and best practice design parameters.

Gul et al. 187



responses, it can be seen that the key focus in atypical practice is capital costs and availablebudget, whereas the running costs and buildingorientation are the key priorities within bestpractice. The results demonstrated that the typ-ically adopted adaptation measure is the use ofair-conditioning in nondomestic buildings wherethe majority of the respondents voted stronglyfor this followed by opening windows and theuse of internal blinds. This initial exercise iden-tified the key issues that are considered duringtypical or best practice design indicating theimportance of these parameters for a successfuladaptation strategy. These include buildingregulations, energy targets, client’s requirementsand internal air quality. Due to the relativelylow response rate, the questionnaire resultswere regarded as indicative only and furtherexploration into these issues was carried out bymeans of FGs and interviews with practitionersto understand their perspective.

Focus groups. The second stage of the processwas to interact with practitioners usingFGs.7,8,12 A diversity of professionals consistingof architects, building services engineers, tech-nical directors, energy consultants and sustain-ability managers were invited to participate inthese sessions. The key objectives of this stagewere threefold: (1) to get an indication of howwelcoming the industry is for an overheatingassessment design tool, (2) to identify practicalways of incorporating the LCF methodologyinto the current design process and (3) to get aconsensus on preferred output indicating therisk for a building overheating. In this regard,four FGs were conducted: two for domesticbuildings FG1 and FG2 (Figure 1) with six par-ticipants each and two (FG3 and FG4) for thenondomestic sector with seven participants each.The FGs indicated that overheating is not per-ceived as a significant risk, currently or in future,for the UK domestic sector. These sessions con-firmed that an overheating risk assessment toolsuch as the LCF tool would have an impact inthe nondomestic sector where the issue is gainingmomentum. Where overheating assessments are

currently carried out, they are usually based onthe steady-state modelling which is required bycurrent regulations and guidelines and nosophisticated dynamic modelling is used to cal-culate current or future overheating risks.

From this study, it emerged that for the toolto be useful and practical it needs to be efficientand in a form which can easily be incorporatedinto existing building modelling software.A plug-in tool to existing software as a checkis deemed very useful. It has to be added tothe way buildings are designed currently andnot be used as a completely separate setup.This study highlighted that a complex anddetailed output, accompanied by a relativelysimple and understandable output, would bethe most useful strategy. A simple plot ormatrix with colour-coded scenarios to indicatethe extent of a building overheating in currentand future climates was suggested as the mostuseful output. The colour-coded risk matrix dis-cussed in Outputs section was a direct result ofthis feedback.

Interviews. To get an in-depth insight and toconfirm the trends that emerged from the ques-tionnaire and FGs, 16 face-to-face interviewswere conducted with building design profes-sionals in two phases.7,8,12,13 Interviews 1(Figure 1) explored the issues in the domesticsector (six in total) and interviews 2 (10 intotal) were conducted with the professionals toexplore the overheating risks for the nondomes-tic sector and in particular mechanically cooledoffice buildings. The practitioners agreed thatthe calculation of overheating is complex asthere is no single fixed definition of overheatingand sometimes overheating in buildings can betriggered by the cumulative effects of other fac-tors e.g. unable to open windows due to con-cerns regarding security, pollution and noise orheat radiated from electrical appliances andbuilding services such as boiler and hot waterstorage. Participants stated that increasedlevels of insulation, air tightness and inadequateventilation can deteriorate indoor air quality,and need to be considered in the calculation.




In the summer, in well-insulated and airtighthomes, a higher rate of ventilation is desirablefor air circulation and regulating internal tem-peratures preferably at all times; though chan-ging summer temperatures in the future mayresult in warmer external air driving a coolingdemand in the home through this supplied ven-tilation. Low internal air quality was deemed topsychologically encourage the perception ofoverheating for the building users as whenpeople feel uncomfortably warm, they may com-plain about the humidity and air quality eventhough these are within normal limits. Theseinterviews further explored the concept of thefuture failure of a cooling plant in office build-ings. It was said that no such future failure existsfor a cooling plant as it has a limited life timeand can be easily replaced. It was said that pro-ducing an optimum solution when it comes todesigning a cooling plant by modelling the entirebuilding is sometimes difficult, as clients usuallydiscourage carrying out significant modelling totest different options as it is time and cost inten-sive. It was stated that most clients think shortterm because it makes economic sense to applythat approach and if a measure is not likely tomake a favourable return within five years thenit is disregarded. The majority of the partici-pants were of the view that a typical clientwould only deem adaptation strategies to bevaluable if enforced by legislation.

Final workshop. To ensure the ‘message’ beingcommunicated is appropriate and to get feed-back on the LCF tool and its outcomes, a finalworkshop was held in London in September2013. The target audience selected includedexperienced building design professionals in theprivate sector as well as in local authorities andhousing associations (HAs), managers withinarchitectural practices and building servicesengineering consultancies and experienced mem-bers of corporate (CE) organisations. This wasto ensure the participants were in a position tofacilitate adoption of the work by their organ-isations with the ultimate effects of (i) signifi-cantly reducing time for evaluating overheating

risk of buildings at design stage and (ii) increas-ing confidence of clients in the resilience of thedesign proposals to future climate change. Bothonline and offline routes were used to promotethe event to reach the target audience. Contactwas made with 98 interested parties leading to35 attendees: 34% from consultancy firms, 29%other academics, 9% local authority (LA), 9%independent industry body, 6% HA, 6% archi-tects, 3% housing developers and 6% other(retired or unknown). The workshop comprisedtwo sessions. Firstly, the research was explainedin a series of presentations across the afternoon.These presentations were given by those whohad led that particular research area, giving con-fidence to the attendees of the knowledge andfamiliarity of each subject.

Secondly, a discussion session asked atten-dees to work together to provide client-basedsolutions to the issue of how to represent over-heating risk. The attendees were split into fourpotential client types based on the individual’sareas of expertise, requested at the time of regis-tering attendance. The discussion required theattendees to work in groups acting on behalfof their particular client type. With eightgroups in total, each client type had twogroups taking part. The groups were to imaginean overheating assessment had been carried out,and that there were six potential output stylesavailable to use (Figure 3).

The client types and the provided scenariosare given below:

1. LA – with a limited budget requires an assess-ment of the risk of future and current over-heating in its schools, with results to bedisplayed as an easy to understand certificate.

2. HA – with varied stock of dwellings needs areport that indicates the likelihood of particu-lar building designs overheating, which iseasily replicable on their website and in com-pany reports.

3. Small–medium enterprise (SME) – with cur-rent focus on a single office building needssome guidance on whether the building canmaintain summer comfort in the near future

Gul et al. 189



LCF probabilistic graph • Full spectrum of risk calculated by the LCF tool based on a chosen failure criteria

• Based on UKCP’09 • Shows several future climate scenarios at

the same time, though a second graph would be required to show any adaptation scenarios

• Allows the client to compare failure criteria on the same graph (e.g. 2% of hours above 28°C rather than 1%)

• Requires dynamic simulation of building(s)

LCF risk matrix

• Simple colour-coded matrix based on the LCF tool (using UKCP’09)

• Allows client to compare multiple future climate scenarios at the same time

• Shows pre and post-adaptation results • Requires dynamic simulation of building(s)

Bar chart

• Simple bar chart for chosen temperature ranges

• Non-probabilistic (based on, for example, UKCIP02 14

"EPC-style" grades • Based on the familiar UK Energy Performance Certificate-style15 rainbow rating, with the arrow denoting where on the scale the building sits

• More suitable for a single building and climate scenario

• Less amenable for use with UKCP’09 directly

• Based on any form of building modelling Qualitative descriptors

• Can be linked to, for example, steady-state Standard Assessment Procedure (SAP) andSimplified Building Energy Model (SBEM)assessments of overheating

• Likely to require simple, non-probabilistic climate information (i.e. less suitable for UKCP’09)

• More suitable for a single future climate scenario, perhaps linked with energy performance assessment

0

10

20

30

40

50

% o

ccup

ied

hour

s

>28°C

25-28°C

Baseli

ne

2030

s

2050

s

2080

s

100

80P

roba

bilit

y of

occ

uren

ce (

%)

60

40

20

00

2080, High

2080, Low

2050, Low

2080, Medium

2050, High2050, Medium

2030, LowCurrent climate

No Adaptation With adaptations

2030, High2030, Medium

5 10 15

Current ClimateMedium Emission - 2030Medium Emission - 2050Medium Emission - 2080

20% of occupied hours > 28°C

% chance of failure

81-10061-8041-6021-400-20

Negligible Risk

Very High Risk

Not significant

Slight

Medium

High

(a)

(b)

(c)

(d)

(e)

Figure 3. The potential overheating analysis output types of the LCF tool.




without the need for additional air-conditioning.

4. CE – with a mixed building stock (office,retail and factory units) needs a detailed ana-lysis on a small number of buildings to guidefuture decisions on heating ventilation andair-conditioning technologies and generalbuilding design.

Preferred choices. For each output type, theattendees suggested advantages and disadvan-tages, and identified a preferred output display.This information was collated so each client hadfour preferred output styles, coming from the twogroups. The five main output types are describedin Figure 3. The attendees were additionallyinvited to highlight a sixth option of their owndesign if the predetermined five options were notsuitable. These are shown as the ‘Other’ optionsin Figure 4. The groups were asked to rankpreferred outputs for their particular client,where each group (with two groups per clienttype) had a first and second choice, giving fourchoices for each client type. This is demonstratedby each row as shown in Figure 5.

For the LA, the preferred output display wasthe risk matrix for decision making, ease ofunderstanding, staged retrofit potential and abil-ity to incorporate adaptations. Equally, theenergy performance certificate (EPC)-style grad-ing was preferred for the ease of display andfitting in with limited LA budgets.

The same output display options were chosenby those representing HAs. The risk matrix waspreferred for estate agents and for existing stock,while the probabilistic graph was preferred fornew-build stock. The ‘Other’ option preferredwas the amended risk matrix on a site plan(Figure 4(c)) of the housing stock, with differentmaps for different adaptations. This could alsoinclude financial aspects for a cost–benefit ana-lysis. The bar chart, EPC-style grading andqualitative descriptors were all too simple asthey assumed identical stock.

For SMEs, the groups liked the risk matrixfor its clear communication, and how it

identifies where adaptations push failure furtherinto the future. It is also a familiar style butmore comprehensive than EPC-style grading.The probabilistic graph was selected to promotein-depth modelling, as it was felt that in-depthmodelling should be done irrespective of cost.The ‘Other’ option preferred included a pyramidof embodied and operational carbon results interms of primary colours (Figure 4(b)).

The groups discussing preferred outputs forthe CE sector were unresolved on the ‘best’option, as each has merit depending on the audi-ence. For technical and professional analysis,the probabilistic graph was preferred. For deci-sion making, the risk matrix was preferred, withthe possibility of altering the colours used to amore traditional red–amber–green scale. Forcommunication purposes, the EPC-style gradingwas preferred, as it is universally recognised.There was felt to be scope for extending thegrading system to show the current situation aswell as the situation in years to come. This isshown in Figure 4(d) and is deemed one of thepreferred outputs.

Discussion and future steps

The academic research output of the LCF toolhas been published previously and resulted in arelatively complex output that would possiblynot be appropriate to a wide spectrum of build-ing professionals. The practitioner feedbackhelped to inform how the LCF tool may bebest used in the building design industry. Eachstep of the interaction (Practitioner Engagementsection) has led to the modification or develop-ment of the tool. For example, the feedback thatthe future overheating assessment is not a cur-rent priority and the insights into the reasons forthis has meant that the LCF tool needs to besimple, easy to use and time and energy efficient.The creation of additional outputs, which sim-plify the probabilistic output, is a responseto feedback from the building industry.12

Throughout this engagement process, it washighlighted that air quality is one of the key par-ameters considered in building design and

Gul et al. 191



Pro

babi

lity

2030s 2050s 2080s

Overheating risk

Operational CO2

Embodied CO2

High

Low

% chance of failure:

81-100

61-80

41-60

21-40

0-20

With adaptation

Without adaptation

Target

2080

2050

Negligible Risk

(a)

(b)

(c)

(d)

Figure 4. The ‘Other’ options: Suggested overheating risk assessment output type for (a) LA, (b) SME, (c) HA and

(D) CE.




therefore reducing the risk of overheating byperforming an additional overheating risk calcu-lation can result in improving the perception ofusers towards air quality.

The final workshop clearly identified that noone output is suitable for all client types. Clientshave different requirements and therefore tailor-ing the outcomes to suit a particular client typewill increase the uptake and affectivity of thetool. It was seen that there is a need to identifythe audience prior to selecting a particularoutput type and there needs to be a balancebetween simplicity, complexity and content.A recurring theme was that the LCF tool isuseful to all the sectors being discussed, andfurther investigation should be made intoexpanding its use. In its current state, the LCFtool is capable of providing in-house consult-ancy services to the interested parties and stake-holders. To provide greater accessibility, the toolcould be further developed and based on practi-tioner feedback,12,13 this may be done poten-tially in three possible ways; (1) incorporatingthe tool into existing DSM packages, (2) formu-lating the tool as a stand-alone piece of softwareand (3) setting up the tool as an open accessonline tool. This however, would require furtherinvestigation and validation where each

possibility has its merits and demerits. Forexample, option 1 means restricting to oneDSM package only and therefore the users ofthat specific package can only benefit in com-parison to option number 3 where an openaccess online tool can attract a large numberof audience, but will require constant updatingto stay efficient. For option 2, proposed softwarecan exploit fast-processing programminglanguages and an efficient user interface.

Conclusions

The recent trend in the building industry hasbeen to reduce the amount of energy neededfor heating by utilising highly insulated fabricand passive solar design. While these measureshave been effective in reducing the space heatingdemand in the winter, they can potentially leadto undesirable overheating in summer currentlyand in future. To deal with the consequences ofthe impact of overheating, knowledge of theprobability of overheating in the future climatewill be a key requisite. The LCF Project investi-gated the use of the UKCP09 climate projec-tions within building performance simulationsand developed a methodology to identify therisk of buildings failing in a future climate, due

LCF probabilistic

graph

LCF risk matrix

Bar chart EPC-style grading

Qualitative descriptors

Other

LA

SME

HA

CE

1st 2nd

1st 2nd

1st 2nd2nd

2nd

1st 2nd

1st

1st

1st 1st1st 1st

2nd2nd

Figure 5. Preferred outputs for clients.

Gul et al. 193



either to excessive overheating or to inadequatecapacity in existing cooling systems. The LCFtool combines dynamic building simulationand probabilistic climate projections, both ofwhich are computationally intensive, in aformat that is potentially compatible with com-mercially available software. The LCF tool notonly assesses the future overheating risks butalso suggests the effect of the adaptation choices,so that the design can be sensitive to the uncer-tainties of future climate projections. The LCFtool can therefore be applied to the simulationresults of any building and use any criterion foroverheating or building failure.

This research has highlighted that the mostimportant drivers considered at the design stageare the thermal characteristics of the building,available budget, comfort criteria and internalair quality. Low internal air quality was alsodeemed to psychologically encourage the percep-tion of overheating for the building users.Respondents of this research exhibited a positiveinterest towards the use of probabilistic climatedata in future designing for tackling overheatingrisks and encouraged further investigation intoexpanding the use of the LCF tool. This paperoutlined the development of the LCF tool anddiscusses the practitioner feedback obtainedfrom industry professionals on the use and applic-ability of the tool, in a final workshop hosted bythe LCF research team in London. The generalconsensus from the final workshop reconfirmsthe outcomes of the FGs where it was said thatthe design tool would benefit from having a sim-pler form of output alongside a more complexresearch output. The qualitative descriptors wereuniversally not selected as preferred outputs asthey are too simplistic and do not containenough information. The LCF probabilisticgraph was considered too complex and expensivefor clients with limited budgets such as HA or LA.Something quite visual and possibly for use in anEPC-style display was thought to be useful, butwith a careful balance to be struck between thecomplex calculations required to draw reliableconclusions, and the ease of display and its under-standing. The LCF risk matrix (or a variation of)

was universally chosen as a preferred output, withits ability to include multiple adaptation vari-ations and include probabilistic information pro-jecting into the future. It represented acompromise between complexity of analysis andthe simplicity of the output. A bespoke building orclient-focused presentation of this matrix mayhowever be preferred for different client types.

All practitioner workshop participants werekeen to see the LCF tool used within the buildingdesign community and could envisage the advan-tages of having a comprehensive yet easily under-stood tool. In its existing form, the tool has a lot ofpotential to be further developed. This will requirefurther investigation to look into avenues such asconverting it into a commercial piece of software,integrating the tool with an existing DSM ordeveloping it as an open access online tool.

Acknowledgements

Appreciation is also extended to Dr Vicky Ingram forsuccessfully organising the final event in London

funded by Heriot-Watt EPSRC Impact AccelerationAwards Grant.

Funding

Low Carbon Futures is part of the Adaptation andResilience to a Changing Climate (ARCC) programmesponsored by the Engineering and Physical Sciences

Research Council (Grant no. EP/F038240/1). Thiswork is also sponsored by Heriot-Watt EPSRC ImpactAcceleration Awards (Grant no. EP/K 503915/1).

Conflict of interests

None declared.

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