Computational building performance simulation for integrated design and product optimization

Transcription

1 Computatonal buldng performance smulaton for ntegrated desgn and product optmzaton Ctaton for publshed verson (APA): Trcka, M., Loonen, R. C. G. M., Hensen, J. L. M., & Houben, J. V. F. (2011). Computatonal buldng performance smulaton for ntegrated desgn and product optmzaton. In Proceedngs of Buldng Smulaton 2011: 12th Conference of Internatonal Buldng Performance Smulaton Assocaton, Sydney, Australa, November 2011 (pp ) Document status and date: Publshed: 01/01/2011 Document Verson: Accepted manuscrpt ncludng changes made at the peer-revew stage Please check the document verson of ths publcaton: A submtted manuscrpt s the verson of the artcle upon submsson and before peer-revew. There can be mportant dfferences between the submtted verson and the offcal publshed verson of record. People nterested n the research are advsed to contact the author for the fnal verson of the publcaton, or vst the DOI to the publsher's webste. The fnal author verson and the galley proof are versons of the publcaton after peer revew. The fnal publshed verson features the fnal layout of the paper ncludng the volume, ssue and page numbers. Lnk to publcaton General rghts Copyrght and moral rghts for the publcatons made accessble n the publc portal are retaned by the authors and/or other copyrght owners and t s a condton of accessng publcatons that users recognse and abde by the legal requrements assocated wth these rghts. Users may download and prnt one copy of any publcaton from the publc portal for the purpose of prvate study or research. You may not further dstrbute the materal or use t for any proft-makng actvty or commercal gan You may freely dstrbute the URL dentfyng the publcaton n the publc portal. If the publcaton s dstrbuted under the terms of Artcle 25fa of the Dutch Copyrght Act, ndcated by the Taverne lcense above, please follow below lnk for the End User Agreement: Take down polcy If you beleve that ths document breaches copyrght please contact us at: openaccess@tue.nl provdng detals and we wll nvestgate your clam. Download date: 09. Mar. 2019

2 COMPUTATIONAL BUILDING PERFORMANCE SIMULATION FOR INTEGRATED DESIGN AND PRODUCT OPTIMIZATION Marja Tr!ka 1, Roel C.G.M. Loonen 1, Jan L.M. Hensen 1, and Jeroen V.F. Houben 2 ABSTRACT 1 Unt Buldng Physcs and Systems, Department of the Bult Envronment, Endhoven Unversty of Technology, The Netherlands 2 Volants B.V., Consultants and Engneers, Venlo, The Netherlands Integrated computatonal buldng performance smulaton (CBPS) can help n reducng energy consumpton and ncreasng occupant comfort. However, the deployment of CBPS n practce has not matured and ts benefts have not been fully exploted yet. Ths paper explores the role of CBPS n product and ntegrated desgn development and optmzaton through two studes. The frst study explores the use of CBPS for product development wthn the scope of clmate adaptve buldng shells. The second study presents a method for assstng the desgn nnovaton process, whch s called Computatonal Innovaton Steerng. INTRODUCTION All over the world there s a need to develop a more sustanable bult envronment. The energy demand and correspondng greenhouse gas emssons keep on rsng, especally n upcomng countres, such as Chna and Inda. Compared to 2005, the World Energy Councl (2007) expects the total prmary energy requrement to be almost doubled by the year of As a response, strct changes n regulatons and desgn strateges have emerged n several countres. Desgners are challenged to come up wth new, nnovatve and non-tradtonal components and ntegrated desgn solutons for buldng and ts systems. Examples of nnovatve components are: clmate adaptable buldng shells (CABS), concrete core condtonng systems, threefold glazng, ground source heat pumps, solar collectors, energy storage systems. However, the development of such nnovatve products and ntegrated system desgns requres an ntegrated approach, concernng desgn methods and phlosophy. Over the last decades, a wde range of computatonal buldng performance smulaton (CBPS) tools has seen the lght and s consdered useful n the ntegrated desgn of nnovatve buldngs and systems. These tools are able to cope wth a number of physcal domans and can be used to study the smultaneous nteracton of both buldng structure and Heatng, Ventlaton and Ar Condtonng (HVAC) systems, whch s consdered mportant for nnovatve product and ntegrated desgn development. However, the deployment of CBPS n practce has not matured and ts benefts have not been fully exploted yet. Besdes for vrtually analyzng and solvng problems after completon of detaled product desgn, CBPS tools could also be used to facltate product development by supportng decson makng processes and assstng nnovaton processes n complex settngs before detal product desgn. In the nnovatve ntegrated desgn process, the desgn team s confronted wth a number of decsons that have to be made regardng the performance of the nnovaton under consderaton. In order to make effectve decsons, the team has to be nformed wth the rght type of nformaton on the rght moment. Partcularly n the begnnng stages of the desgn nnovaton process, where the level of uncertanty s the largest and major desgn changes can stll be done, useful desgn nformaton must be generated. By quantfyng the uncertantes or unknowns and ther mpact on the performance of the desgn, the complex decson makng can be supported wth nformaton n the form of rsks and opportuntes. Thus, CBPS, together wth utlty functons and senstvty and uncertanty analyses are consdered promsng nstruments for generatng ths type of performance nformaton. In ths paper, we present two case studes that use CBPS for nnovatve product development and nnovatve ntegrated desgn optmzaton. CBPS FOR PRODUCT DEVELOPMENT The frst study explores the use of CBPS for product development wthn the scope of CABS. CABS have the ablty to change ther propertes and behavor over tme. Provded they are desgned and operated effectvely, CABS offer the potental for energy savngs wthout the need for compromsng comfort levels. Ths part of the paper explores the potental role that CBPS can play n product development by takng the nnovatve wndow technology Smart Energy Glass (SEG) as an llustratve case study. Smart Energy Glass SEG s based on a polymer coatng that s placed between two layers of glass. These layers together form the external pane of an nsulated glazng unt. By applyng an external voltage to the coatng, t s possble to control the optcal propertes of the

3 wndow wthn less than a second. SEG can be swtched nto three dfferent states: a brght state, a dark state and a translucent state. The polymer coatng n SEG also acts as planar wavegude n the same way as a lumnescent solar concentrator (LSC) (Goetzberger and Greube, 1977). The dye molecules absorb part of the ncomng sunlght and re-emt photons n random drecton at a longer wavelength. Va ths mechansm, part of the ncomng lght s captured and redrected to the edges of the wndow where photovoltac cells are stuated to convert the collected radaton nto electrcty. SEG s currently not a market-ready commercal product, and s stll under development. Research and development actvtes currently focus on optmzng absorpton and emsson spectra, thermal performance of the wndow, optcal losses, electrcal crcuts, etc. CBPS can help n the optmzaton process snce t provdes ntegrated vew of the performance and facltates dentfcaton of promsng product solutons for specfc applcatons. Modelng SEG The work presented n ths paper complements and nteracts wth the development actvtes by provdng computatonal support for assstng the nnovaton processes n product development. For ths purpose, a model of SEG that ntegrates ts electrcal, thermal and optcal propertes s created. In ths study, electrcal model s based on emprcal knowledge obtaned after conductng dedcated experments, whch were set-up wth the emphass on elucdatng SEG s behavor under dfferent lght ncdent angles. The detals of the experment, model descrpton and ts valdaton can be found n Loonen et al. (2010). The optcal propertes of SEG are altered by changng the global algnment of molecules n the dye. Characterzaton of these propertes n brght and dark state was done for SEG samples n an expermental set-up (Loonen et al. 2010). Values for reflectance and transmttance were measured accordng to the protocols n ISO 9050 (2003). Ths data was then transformed nto spectrally averaged propertes wth the ad of the software tool OPTICS (LBNL, 2010). Thermal wndow propertes were establshed by usng the complementary software tool WINDOW5 (LBNL, 2010). Smulaton strategy for SEG After revewng capabltes of avalable state of the art CBPS tools, we have opted to use TRNSYS (TRNSYS, 2011) for performance predcton n the thermal and electrcal doman, and to couple ths model wth the results of dynamc daylght smulatons n DAYSIM (DAYSIM, 2011). Fgure 1 shows a schematc representaton of the smulaton strategy that s used for performance predctons of SEG. Daylght smulatons are frst conducted n a preprocessng stage for all wndow states ndependently. Annual tme-seres of fve mnute lumnance and llumnance values at specfc sensorponts are then suppled to TRNSYS that selects the rght data durng run-tme correspondng to the controlled wndow state. Data s flowng across the three domans at every tme-step and therefore data exchange s dynamc. The smulators n DAYSIM and TRNSYS are however nvoked consecutvely and therefore the workflow s sequental. Ths approach s justfed by the short-term dynamcs of daylght performance that does not suffer from hstory effects. The control component plays a central role n SEG s smulaton model as ndcated n Fgure 1. Every tme-step, output data from DAYSIM s accessed by the control component whch decdes upon the rght adaptve actons on the bass of an mposed control strategy. Fgure 1: Smulaton strategy for the SEG model. Ths control logc s mplemented va equaton-types contanng condtonal statements that compare model output (e.g. temperature, wndow lumnance, workplane llumnance) to target values and return wndow ID for the next tmestep as output. Ths wndow ID s passed on to the thermal and electrcal model and used n the respectve calculatons for the next tme-step. The wndow state, together wth ncdent radaton results n amount of collected radaton va the use of equaton-types. Ths energy flux s then converted nto electrcty va a photovoltac array model (TYPE 180). The nfluence that SEG exerts on thermal performance of a zone or buldng s evaluated by usng the TRNSYS mult-zone buldng model (TYPE 56). In the smulatons, wndow propertes are changed durng run-tme wth a functon called varable wndow ID. Envronmental condtons are ensured to be dentcal to those subject to the daylght

4 model by selectng the same weather fle. Internal heat gans of the buldng depend on the state of the wndow snce the amount of artfcal lghtng changes wth daylght avalablty. Together wth occupants presence, ths data s mported from the data fles pre-calculated by DAYSIM. Analysng SEG performance for two-person offce The analyss n ths study s restrcted to a two-person south-facng permeter offce zone (3.6m x 5.4m x 2.7m), stuated at an ntermedate floor, and s assumed to be surrounded by dentcal offce spaces. These adabatc boundary condtons were selected to ascertan that observed performance dfferences are effectvely attrbutable to SEG. Storage of thermal energy n nternal parttons s taken nto account, and typcal offce equpment amounts to a heat load of 10 W/m 2. The wndow-to-wall rato of the south façade equals 35 %. Occupancy n the offce room follows DAYSIM s probablty-based fve day work-week schedule wth ntermedate and lunch breaks. Artfcal lghtng (15 W/m2 nstalled power) swtches accordng to ths same schedule and s contnuously dmmable up to an ndoor llumnance of 500 lx on the bass of the LIGHTSWITCH-2002 algorthms (Renhart, 2004). Lghtng control s trggered by a work plane photosensor at a dstance of 1.8 m from the envelope. Thermal condtons n the zone are controlled on the bass of ndoor ar temperature, wth setponts for heatng (20 o C) and coolng (24 o C) between 8 a.m. and 17 p.m., and a heatng setpont of 16 o C outsde workng hours. Stochastcally generated short tmestep (fve mnute) solar rradance data fles (Walkenhorst et al., 2000) are created once for every case, and are used for predctng daylghtng performance. Ths study evaluates the potental to use SEG as wndow replacement n a renovaton case. The reference case assumes conventonal double glazng and opaque constructon elements wth typcal nsulaton standard for offce buldngs constructed around 1975 (Petersdorff et al., 2006). Solar shadng and brghtness control n the reference case s acheved va manually controlled nternal venetan blnds. Operaton of blnds n the smulatons s deally controlled n DAYSIM on the bass of the Actve users profle. Ths stochastc algorthm assumes that blnd settngs are rearranged on a regular bass wth the am of maxmzng daylght avalablty whle excludng glare (Renhart, 2004). The number of possble strateges for controllng SEG s adaptve behavor s vrtually nfnte. The am of ths paper s to explore ther potental and provde some frst nsghts n the cause and effect relatonshps of varous optons. Table 1 provdes an overvew of nvestgated control strateges. The energy savng potental of SEG s assessed by consderng overall annual energy demand, subdvded n terms of energy requred for heatng, coolng and artfcal lghtng. Table 1: Lst of the nvestgated control strateges A B C D E Reference case SEG always swtched n the brght state SEG always swtched n the dark state SEG swtched to the dark state when ndoor ar temperature " 21! C SEG swtched to the dark state when daylght llumnance on work plane (Eh) " 700 lx F SEG swtched to the dark state when wndow lumnance (Lv) " 1500 cd/m 2 A second performance ndcator () s peak heatng and coolng demand. Savng energy s however only acceptable when ths occurs n absence of dscomfort. Consequently SEG s mpacts on comfort are at least equally mportant. In ths paper, thermal comfort s assessed on the bass of overheatng rsk. Ths s accomplshed by countng the number of hours that ndoor ar temperature exceeds 25 o C. Allowng dscomfort durng maxmum 5 % of workng hours s usually seen as realstc and economc target value n the trade-off between energy and comfort. As a result, ths amounts to an allowed number of 100 overheatng hours. Vsual comfort s evaluated by consderng the rsk of glare, whch s defned as the sensaton produced by lumnance wthn the vsual feld that s suffcently greater than the lumnance to whch the eyes are adapted to cause annoyance, dscomfort or loss n vsual performance and vsblty (IESNA, 2000). In ths study, the rsk of dscomfort caused by glare s assessed by countng the number of tmes when the rato between wndow lumnance and paper task lumnance s hgher than 10:1 (IESNA, 2000). Results Fgure 2 shows annual energy demand and comfort performance for each of the sx cases as gven n Table 1. The results suggest that coolng energy demand after wndow replacement wth SEG s cut by more than a factor two. In addton, nstalled coolng power capacty was found to be safely reduced wth more than 30 % (from 1.0kW to 0.67kW) when SEG s nstalled, whle stll achevng suffcent thermal comfort levels. Fgure 2 further shows that heatng energy demand for the basecase (case A) compares well to that for SEG, even though the wndow U-value of SEG s lower as a result of the presence of a low-e coatng. Closer nspecton at the energy balance reveals that the transmsson losses after renovaton are ndeed lower, but ths dfference s almost compensated by the decrease n valuable passve solar gans. The lower vsble transmsson values of SEG also gve rse to a relatvely large electrcty demand for

5 lghtng. The results further show that occurrence of glare n the reference case s comparatvely hgh. Ths s caused by the fact that () the wndow has a hgh vsble transmttance, and () blnds are operated manually. Fgure 2: Comparson between energy and comfort performance of the reference (A) and SEG (case B to F) n an advanced renovaton case. Wth on the left axs: heatng, coolng and lghtng energy consumpton [kwh], and on the rght axs: rsk for overheatng [h] and glare [tmes]. Consderng SEG as an alternatve can provde an adequate soluton for fulfllng both thermal and vsual comfort requrements. If contnuously n the brght state (case B), total energy consumpton gets reduced, but glare s stll a problem. When always swtched to the dark state (case C), the occurrence of glare drops drastcally, but at the same tme ths also ntroduces an undesrable hgher energy demand for artfcal lghtng. Implementaton of an approprate control strategy (e.g. case D to F) s the key that allows for proftng from the benefts of both the dark and the brght state. Apparently, best results are obtaned when wndow state s controlled based on stmul from the lumnous envronment (case E or F). In these cases, daylght s only allowed when desred and blocked when unwanted. Dscusson The CBPS model s used to evaluate the potental of SEG subject to varous control strateges and provdes suggestons for future research and development of SEG. On the bass of the presented smulatons t s not yet possble to gve conclusve answers about SEG s energy savng potental. The concept however seems promsng because energy savngs can be acheved whle at the same tme comfort levels mprove. On the other hand, the results do also suggest that there s stll room for product mprovements, such as: Swtchng the optcal propertes of SEG prmarly takes place n the vsble wavelength area. Blockng solar gans s therefore followed by a proportonal ncrease n lghtng energy use. The net result s that solar gans are exchanged for nternal gans, and consequently part of the energy savng potental s counterbalanced. The lumnescent dye technology however makes extenson of the swtchng range to other parts of the spectrum vable. The ultmate soluton would be a wndow that s capable of swtchng vsble and nfrared transmttance ndependently. Research efforts pursung ths am are underway, but currently the lumnescent materals actve n the nfrared wavelength area stll suffer from low stablty, low quantum effcency, and a relatvely small absorpton spectrum (Goldschmdt, 2009). The bandwdth for swtchng SEG s relatve narrow compared to other swtchable wndows. In addton, SEG only swtches n ether one of three states, wthout the possblty for gradual transtons n between. The produced amount of electrcty s two orders of magntude lower compared to the scale of hundreds of kwh n Fgure 2. Ths has motvated further product development as self-suffcent SEG wthout consderaton of further dstrbuton of the generated electrcty. SEG s stll n the prototype phase, and more work s requred towards optmzaton of the fnal product. CBPS can help to dentfy the focus of attenton for future product development n the laboratory as well as n the actual ntegraton n the buldng shell. Currently, we are usng the smulaton model presented n ths paper to nvestgate whch optcal and thermal propertes and whch control strategy yeld the optmum performance of SEG for dfferent applcatons. CBPS FOR INTEGRATED DESIGN DEVELOPMENT The second study presents a method for assstng the desgn nnovaton process, whch s called Computatonal Innovaton Steerng (CIS). CIS makes use of CBPS and moreover focuses at the applcaton of uncertanty analyss, senstvty analyss and rsk and opportunty analyss as promsng tools for ths purpose. Prncples of CIS procedure Based on de Wlde (2004) the followng assumptons are made pror to developng CIS procedure: Desgn decsons are based on a multple of desgn alternatves or optons. The decson between alternatves has to be made on bass of multple crtera (.e., performance ndcators or performance aspects). For each desgn opton the same performance nformaton must be avalable. CIS dffers from the approach of de Wlde (2004), due to applcaton of UA and SA technques, rsk analyss, ts scope and the use of utlty elctaton for every performance ndcator

6 An extended verson of the developed CIS procedure s llustrated n Fgure 3. In ths research the optmzaton step n the smulaton phase was consdered for future work and therefore not mplemented n the prototype envronment. Fgure 3: Overvew of the developed CIS procedure Defnton phase The frst phase of CIS s concerned wth () the defnton of performance, () the creaton of an opton space and () the elctaton of utlty functons. Performance s descrbed by dvdng t nto objectves, performance ndcators, acceptable ranges and requrements. An objectve s the translaton of a desgn task nto specfc goals to be acheved by the desgn team and a Performance Indcator () s a quantfed objectve, havng an acceptable range, defnton, unts and a drecton of ncreasng or decreasng value. Step two of the defnton phase s concerned wth the generaton of an opton space. When the requred performance s defned, the desgn team can start developng desgn optons. The opton space comprses the collecton or set of all possble desgns (Struck et al., 2009). Creatng an opton space stmulates creatvty and can be supported by a number of technques, such as branstormng, mndmappng, morphologcal charts or automated approaches, such as genetc algorthms for performng parameter varatons (Gres, 2004). The thrd part of the defnton phase s the elctaton of utlty functons. Utlty functons make t possble to capture user preferences over the acceptable range of a (Keeney and Raffa, 1993). Both utlty values and probablty values are needed to compute rsks and opportuntes. Smulaton phase In the second phase, CBPS tools are used to predct the performance of the proposed desgn optons. CBPS s accompaned by the use of SA and UA amng to generate more nsght and therefore useful desgn nformaton. Step 1: In CIS, the goal of SA s twofold: () selectng the most mportant desgn parameters and () reducng the opton space. Monte Carlo smulaton wth regresson s the method of choce for the SA n the CIS procedure (Saltell et al., 2004). Unform nput dstrbutons coverng a relatvely wde range are suppled to the smulaton model. Step 2: When dealng wth desgn nnovatons, the desgn team s confronted wth many new deas and aspects, and a lmted amount of nformaton regardng the performance of the desgn nnovaton s avalable: the desgn nnovaton process s thus very uncertan. Consequently, the desgn team s deemed to make desgn decsons, based on an ncomplete set of nformaton. Therefore, t s useful to quantfy the uncertantes. In ths way, better-nformed decsons can be made, leadng to possbly better desgns. Ths step starts wth a defned set of desgn parameters 1 and gven uncertanty of those recognzed as nfluental n the step one of the second phase n CIS procedure. Agan Monte Carlo smulaton s appled, but ths tme, probablstc nput dstrbutons are fed through the models (Saltell et al., 2004). Typcally, normal dstrbutons are used, where the mean values of the model parameters are vared over a small nterval (n the order of fve percent). Samplng s done by means of the Latn Hypercube method, because ths delvers satsfactory results wthn a mnmum number of samplng runs (Hopfe, 2009). The result of the UA s a number of probablty dstrbutons for each of the consdered s that can be used n the next step: determnng performance rsks and opportuntes. Step 3: Rsk and opportuntes are the actual forms of nformaton that are to be generated wth the help of CIS. In the lght of CIS, the rsks and opportuntes refer to the (un)certantes that are assocated wth the (lack of) knowledge about the techncal performance of the desgn nnovaton that s nvestgated. Ths concept of rsk and opportunty has been nspred by the work of Smalng and de Weck (2007). Rsk can be defned as the lkelhood that somethng happens tmes the correspondng consequence of t (Houben, 2010): R =p I =! p (x ) ( U ( xt )-U ( x )), (1) where p s the probablty that a certan value occurs (-); I s the consequence (or mpact) correspondng to the probablty U x s p (-); ( ) the utlty correspondng to the requred targetvalue U x s the utlty correspondng to x T (-); ( ) x (-). the actual value 1 The defnton of ths set can be done usng optmzaton. The dentfed s are used as objectve functons and the set of the parameters recognzed as nfluental n the step 1 of the second phase n CIS procedure are used as decson varables. T

7 CIS prototype procedure Fgure 4: Defnton of rsk It can be observed from the above defnton that the mpact s a functon of the gap between the target utlty and the actual utlty. Fgure 4 shows a graphcal representaton of the rsk defnton for a smaller-s-better type (a smaller value s consdered postve n ths case). Opportunty s the lkelhood that a certan -value (performance) occurs and s therefore present over the entre acceptable range. Opportunty can be defned usng the followng formulae (Houben, 2010): O =U ( xt )! p (x ) U ( x ), (2) where ( ) T U x s the utlty correspondng to the requred value x T. The overall rsk and overall opportunty are obtaned by means of a weghted sum approach: R=! α R, and O=! α O (3) where α s the relatve weghtng factor for to be defned by the stakeholders, R s the rsk correspondng to and the opportunty correspondng to. Step 4: Another step n the smulaton phase s optmzaton. Desgn optmzaton s an nterestng way to deal wth conflctng objectves, whch are often encountered n desgn nnovaton. Desgn optmzaton was, however, consdered to be a research on ts own and therefore not mplemented n the CIS prototype procedure presented here. Nevertheless, optmzaton can be an nterestng way to search for new desgn optons (Hopfe, 2009). Notce that all rsks and opportuntes are calculated for the same s, so the comparson s done n a ratonal way (based on one set of multple crtera). Decson phase When the rsks and opportuntes of all proposed desgn optons are calculated from the smulaton results and utlty functons, a decson can be made. By placng the results n a rsk-opportunty plot (Smalng and de Weck, 2007), a drect comparson between all optons s possble. Fgure 5: Overvew of the prototype workflow for the SA, rsk and opportunty analyss. The CIS procedure, llustrated n Fgure 3, was mplemented nto a software prototype. The tools Matlab /Smulnk, TRNSYS (16.1) and Smlab were employed for the smulatons, generatng nput samples, performng the SA, UA and post-processng of the results. Smlab s a statstcal pre- and postprocessor useful for performng SA and UA and has been appled successfully n the past by a number of researchers (e.g. Hopfe 2009). In Fgure 5 the workflow for the SA, rsk and opportunty analyss s llustrated. Illustraton of CIS procedure n buldng desgn An nnovatve offce buldng desgn, called Vlla Flora, was selected for the case study. The hghambtous project s a desgn by archtect and nventor prof. J. Krstnsson, and s planned for constructon at the Florade hortcultural exhbton n 2012, n Venlo, the Netherlands (Krstnsson, 2007). Essentally, the Vlla Flora desgn concept conssts of a combnaton of an offce buldng and a greenhouse wth a number of artfcal clmate zones (Sahara, Medterranean, Amazone), whch s consdered benefcal for the heat balance n the buldng. A range of nnovatve HVAC and energy systems are part of the studed buldng desgn (Krstnsson, 2007): double-deck concrete floors wth Concrete Core Condtonng (CCC), hghly effcent heat exchangers (Fwhex) for very low temperature heatng and hgh temperature coolng, decentralzed ventlaton unts (Breathng Wndow) wth heat recovery, usng the same Fwhex heat exchanger technology, parabolc pv/thermal collectors, for combned heat and electrcty generaton. The detals of the smulaton model can be found n Houben et al. (2010). In ths paper we dentfy overheatng hours and HVAC electrcty consumpton as representatve s. The overheatng

8 hours s defned as the number of hours n a year that the ndoor ar temperature s allowed to be hgher than a specfed threshold value. For the varous clmate zones of the Vlla Flora buldng, dfferent threshold values were chosen. The HVAC electrcty consumpton s the amount of electrcty needed to operate all auxlary pumps, fans and valves, contaned n the hydraulc crcuts of the CCC and Fwhex systems. For the case study n ths research, utlty functons were elcted wth a software tool, called Assess, whch uses the Lottery Equvalents method (Delqué, 2010). The elcted utlty functons for an experenced HVAC desgner for the s overheatng hours and HVAC electrcty consumpton are shown n Fgure 6. The assessment ponts gven n the fgures were obtaned wth the help of structured utlty ntervews. After assessment, the utlty functons were ftted between the assessed ponts. In ths research, also the nfluence of dscontnuous (or bnary) utlty functons on the resultng rsks and opportuntes was nvestgated (Hu, 2009). the complex decson-makng process usually present n desgn nnovaton. CIS procedure enables desgners to: reduce the parameter space, ndcate and focus on the most mportant desgn parameters, steer the nnovaton process by provdng useful desgn nformaton, n the form of R/O plots, tornado dagrams, probablty dstrbutons and utlty curves. Fgure 6: Example of derved utlty functons of an nexperenced for s overheatng hours and HVAC electrcty consumpton SA results (Fgure 7) revealed that the performance of both the CCC and Fwhex systems s hghly dependent on the water supply temperatures and medum flow rates. Therefore, two control strateges of the supply water temperature were proposed as new desgn optons for the second CIS cycle: () control of the supply water temperature as functon of the ndoor temperature (case2a), () control of the supply water temperature as functon of the ambent temperature (case2b). In Fgure 8 the Rsk/Opportunty plot for all desgn optons and concernng three types of utlty functons, s gven. The opportunty s calculated accordng to Equaton (2). From the Rsk/Opportunty plot (Fgure 8) t can be notced that an actve control strategy of the supply water temperature as functon of the ambent temperature seems to be the most promsng desgn opton for the HVAC system desgn (.e. t ndcates the lowest rsks and hghest opportuntes for all three types of utlty functons and each of the consdered equatons for the opportunty calculaton). Dscusson CIS offers desgners the opportunty to generate useful desgn nformaton that can be put forward n Fgure 7: Tornado plots for base case - overheatng hours and HVAC electrcty consumpton (SRC = Standard Regresson Coeffcent) Fgure 8: Rsk/opportunty plots for two dfferent formula

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