I N D U S T R I A L P H D - P R OJECT W I T H M OE A / S PHD STUDENT TORBEN ØSTERGÅRD

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1 PRE-DESIGN INFORMATIVE BUILDING SIMULATIONS FOR IMPROVING THE EARLY DESIGN PROCESS I N D U S T R I A L P H D - P R OJECT W I T H M OE A / S PHD STUDENT TORBEN ØSTERGÅRD

2 Agenda A b o u t m e C h a l lenges p e r s p e c t i v e o f t h e a r c h i t e c t u r a l e n g i n e e r P r o p o s e d m e t h o d s U n c e r t a i n t y a n a l ys i s ( U A ) a n d sensitivity a n a l ys i s ( S A ) S t a t u s o n t h e project E xamples o n pre-design i n f o r m a t i o n Output distribution ( U A ) S e n s i t ivity a n a l ys i s M o n t e C a r l o Filtering W h a t s n e xt Questions a n d comments? P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

3 About me N a m e / n i m i = To r b e n Østergård I n d u s t r i a l P h D student - E n r o l l e d at A a l b o r g U n i v e r s i t y, D e n m a r k - E m p l o ye d b y C o n s u l t i ng Engineering, D e n m a r k & Norway, ~ employe e s 3 ye a r s J u n e 2014 M a y S p a r e t i m e (hopefully)? - S p o r t s ( c l i m b i n g ), t r a v e l l i n g a n d much more P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

4 Challenges (some of them) I n c r e a s i n g r e g u l a t o r y demands M a n y objectives a n d increasing use o f L E E D, B R E E A M, D GNB N e e d for holistic a p p r o a c h M u l t i p le s o f t ware packages a n d time-consuming m o d e l g e n e r a t i o n C o o p e r a t i o n with architects K e e p u p with the pace o f c h a n g e i n e a r l y d e s i g n p h a s e P r o v i d e p r o mpt a n d relevant d e c i s i on s u p p o r t F o r e s e e c h a l l e n g es and/or c o n s e q u e n ces F o r e s e e f a v o r a b l e d e s i g n i n p u t r e g i o n s D e v i a t ions from e xpected a n d measured building p e r f o r m a n c e P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

5 Methods of interest Global s e n s i t ivity a n a l ys i s ( S A ) and uncertainty analys i s ( U A ) S t o c h a s t ic m o d e l l i n g with i n p u t d i s t r i b u t ions m o r e i n f o r m a t i o n D e m o n s t r a t e worst-case / best-case s c e n a r i o s H i g h light most important i n p u t ( c o n c u r r e n t l y c h a n g i n g ) I n d i c a t e f a v o r a b l e a n d unfavorable r e g i o n s o f i n p u t d o m a i n I n d i c a t e d e s i g n r o b u s t n e s s E v a l u ate s e p a r a t e ( r e l e v a n t ) objectives s i m u l t a n e o usly e. g. e n e r g y consumption, o v e r h e a t i n g, d a yl i g h t f a c t o r, e m b o d i e d e n e r g y etc. Generate knowledge-based input d a t a b a s e s f o r q u i c k e r m o d e l l ing P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

6 UA / SA A quickie P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

7 Status on the project P r o t o t yp e using E xcel V B A a n d simple e n g i n e s ( u s e d f o r b u i l d i ng c o m p l i a n ce i n D e n m a r k ) E n e r g y model B e 1 0 ( E N , DK) T h e r m a l m o d u l e S u m m e r c o m f o r t ( E N , DK) Glass/floor r a t i o s ( d o m e s t i c b u i l d ings, D K ) P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

8 Setting up input Input til UA/SA DistributionMin Max Steps ID ncludebundleparameter Unit Category _dist1 _dist2 _dist3 _dist4 #1 1 Heat capacity Wh/K m² Quality Steps #4 1 U-value, roof W/m² K Quality Steps 0,08 0,15 10 #5 1 U-value, terrain W/m² K Quality Steps 0,1 0,15 10 #6 1 U-value, floor W/m² K Quality Steps 0,07 0,12 10 #7 1 U-value, wall W/m² K Quality Steps 0,1 0,2 10 #8 1 Linear heat loss, winw/m K Quality Steps 0 0,03 6 #9 1 Linear heat loss, fouw/m K Quality Steps 0,2 0,6 8 #10 1 Linear heat loss, balw/m K Quality Steps 0,1 0,4 6 #11 1 U-value, windows W/m² K Quality Steps 0,6 1,2 12 #12 1 g-value, windows - Quality Steps 0,4 0,65 10 #13 1 Frame factor - Form and function Steps 0,7 0,9 8 #14 1 Shading factor - Form and function Steps -0,6-0,95 10 #15 1 Horizon % Robustness Steps #16 1 a Fins, left % Form and function Uniform #17 1 a Fins, right % Form and function Uniform #18 1 Overhang % Form and function Steps #19 1 Opening % Form and function Steps #20 1 Ventilation, qvm l/s m² HVAC Steps 0,3 0,5 4 #21 1 Ventilation, qvm, dal/s m² HVAC Steps 0,3 0,5 4 #22 1 Venting, day l/s m² HVAC Steps 0,9 3,6 12 #23 1 Venting, night l/s m² HVAC Steps #28 1 b People load W/m² Robustness Steps #29 1 b Equipment load W/m² Robustness Steps Total 23 P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

9 Setting up output ID Include Parameter Unit Function 1 1 Net energy demand kwh/m² Energy demand, DK regislation kwh/m² 3 Heating demand kwh/m² 4 DHW demand kwh/m² 5 Electricity demand kwh/m² 6 Overheating kwh/m² 7 Lighting kwh/m² 8 Ventilation kwh/m² 9 Cooling kwh/m² 10 Minor electricity contributions kwh/m² Thermal comfort, h>26 C h 12 Thermal comfort, h>27 C h 13 Glass/floor-ratio % 14 1 Transmission loss (envelope) W/m² 15 Holistic score % P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

10 Run simulations 1 ) Choose s a m p l i n g strategy ( r a n d o m, L H S, Sobol) 2 ) Choose n u m b e r of simulations t h e more, the merrier 3 ) Run simulations s i m u l a t ions ~ 1-2 m i n u t e s ( o n l a p t o p ) P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

11 Output distribution (uncertainty) BEST CASE Choosing the best input WORST CASE P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

12 Sensitivity analysis 23 input, 3 output Net energy demand h>26 C 0 Transmission loss, W/m² P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

13 Sensitivity analysis 23 input, 3 output R a n k i n g a n d quantitative s e n s i t i vity R P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

14 Slide 13 RLJ4 Mærkeligt at solafskærmning kun er #7 - men det må vel ha' noget med inputfordelingerne at gøre Rasmus Lund Jensen;

15 Monte Carlo Filtering W h i c h f a c t o r o r g r o u p o f f a c t e r s a r e m o s t r e s p o n s i b l e f o r p r o d u c i n g m o d e l o u t p u t s w i t h i n o r o u t s i d e s p e c i f i e d b o u n d s ( S a l t e l l i e t a l., ) I n p u t s p a c e O u t p u t s p a c e Two subsets for input space, X i : (X i B) produce behavioural (acceptable) results Two subsets for output space, Y: B represent behavioural results P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

16 Monte Carlo Filtering RLJ5 1. S e t u p u n i f o r m i n p u t d i s t r i b u t i ons 2. P e r f o r m simulations r e p r e s e n t i n g a l a r g e o p t i o n s p a c e 3. F i l t e r r e s u l t s u s i n g c o n s t r a i n t s / r e q uirements 4. Observe which i n p u t a r e m o s t likely t o p r o d u c e a c c e p t a b l e r e s u l t s P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

17 Slide 15 RLJ5 Den her slide forstår jeg ikke - men din forklaring hjælper nok på det Du har 1-4 i punktopstillingen og a-d i figurerne!? Rasmus Lund Jensen;

18 Example: SA + MCF Ordered by s e n s i t ivity 2 2 p a r a m e t e r s (uniform dist.) F i l t e r e d b y 1 0 % b e s t (holistic) R e c o m m e n d a t ions based on h i s t o g r a m s P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

19 Monte Carlo Filtering I n p u t - i n p u t c o r r e l a t i o n s E xample: s i m u l a t i ons > s o r t i n g t o p 1 0 % P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

20 Parallel coordinate plot P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

21 Parallel coordinate plot FILTER Max 250 h > 26 C P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

22 Parallel coordinate plot FILTER Heat capacity max 120 Wh/m2 floor area P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

23 What s next? C o n t i n u e l i t e r a t u r e r e v i e w Te s t p r o t o t yp e o n domestic b u i l d ings F i n d a p p r o p r i a t e s i m u l a t ion s o f t ware (hourly based) C r e a t e k n o wledge b a s e d i n p u t d a t a b a s e s f o r q u i c k m o d e l l ing P r o g r a m m i n g p l a t f o r m? P ython, C#, VB, web, java? I n t e g r a t e o p t i m i z a t ion t e c h n i q u e s? U n k n o wn/known c h a l l e nges M u l t i p le z o n e s h o w to calculate a n d evaluate? B I M compatibility? S p e e d o f s i m u l a t ion c l u s t e r i n g o r c l o u d c o m p u t i n g? A r c h i t e c t s n e e d s, c o m m u n i c a t i on f o r m, v i s u a lization, e t c.? P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

24 Questions and/or critical remarks? c c P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /

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26 I n f o r m e d decision making, t e r m p r e - d e s i g n D M ( a d a p t e d from A t t i a ) I n p u t - i n p u t p a r a m e t e r s E xample: i f PV p o s s i b l e n o n e e d t o o p t i m i z e ( S A ) on several p a r a m e t e r s, b u t i n s t e a d f o c u s o n challenged a r e a s (SA i m p o r t a n t p a r a m e t e r s f o r t h e r m a l, dayl i g h t, e t c. ) M o n t e C a r l o Filtering P R E - D E S I G N I N F O R M A T I V E S I M U L A T I O N S P A G E / 0 9 /