Building energy controlled by the reflective facades

Transcription

1 1 IBPSA Nordic seminar, 19 th -20 th September 2013, Lund university 0 Building energy controlled by the reflective facades -CONTENTS- 1. Introduction 2. Method 3. Results and discussions -Simulation work -Multiple regression analysis 4. Conclusions NTNU, Norway Takeshi Ihara Introduction usolutions for Energy efficient buildings ujapan: high density, high rise building, cooling/heating 1 uhighly reflective materials Tokyo, Japan

2 2 Introduction The current researches umain concern : Roofs (many papers) 2 Gray urecent concern: Facades (Not many papers) -Not many researches were done. -Energy benefit was pointed out. Gray Purpose 3 uthe investigation of the energy benefits in quantitative way uthe easy method to predict the energy benefit -The location was fixed to be Tokyo, Japan -relatively high rise buildings

3 3 Method Metal wall model (mm) Exterior Aluminum panel Air layer Concrete Polyurethane Air layer Gipsum board Interior Wall specification - U= Exterior solar reflectance: 0.1, 0.4, 0.7 Method Heat flux simulation 5 - WUFI - Heat flux in summer, winter

4 7 b 4 Method Annual energy simulation u WUFI Plus No. Parameters contents 1 The number of floors 5, 15, 30 floors 2 A floor aspect ratio 0.49, 1.00, A floor 1225, 2401, 3969 m 2 4 The ratio of the window to the wall 10, 20, 40% 5 The wall solar reflectance 0.1, 0.4, 0.7 u The fixed parameters - -Rectangular buildings (north-south direction) were considered. -Location: Tokyo, Japan -U value: 0.86(roof), 1.99(slab), 3.39(intermediate slab) u 243 (= 3 5 )models were simulated. 6 Method Annual energy simulation u A floor plan, a floor aspect ratio Floor Floor A floor : m 2 A floor : A floor : m 2 shape 7 Oblong separation Floor plan ratio (b/a) = Square Floor plan ratio (b/a) = (North) Oblong Floor plan ratio (b/a) = Sub facade (East) (m) a Main facade (South) Sub facade (East)

5 Method Annual energy simulation u The ratio of window to wall 8 Facade fenestration for office Facade fenestration for side Wall Wall Large window Window ratio = Floor level Window 2 m 4 m Floor level Window Core width 2 m 4 m Building width Building width Wall Wall Medium window Window ratio = Floor level Window 2 m 4 m Floor level Window Core width 2 m 4 m Building width Building width Wall Wall Small window Window ratio = Floor level Window 2.5 m 0.5 m 4 m Floor level Window Core width 2.5 m 0.5 m 4 m Building width Building width u Reflective double window: U-value 2.73, SHGC: 0.5 Method Annual energy simulation 9 u Inner loads, HVAC design for office 5

6 6 Method Annual energy simulation u Model images 10 Results and discussions Heat flux simulation (outermost surface) -Heat flux reduction in summer and winter 11

7 7 Results and discussions Heat flux simulation (innermost surface) -Heat flux magnitude is less -Same phenomenon (heat flux up/down, summer/winter) 12 Results and discussions Annual energy simulations -Similar results -Less annual energy (decreased cooling >increased heating) -Linear relationship 13 Window: Medium type fenetration A floor : 3969 m 2 Annual A energy demand (kwh/m 2 ) Number of floors: Solar reflectance 100 Number of floors: 15 Number of floors: Solar reflectance 80 0 a floor aspect ratio: a floor 0.4 aspect ratio: a floor aspect ratio: Oblong plan (15 floors) Oblong plan (30 floors) Solar reflectance Solar reflectance a floor aspect ratio: 2.04

8 8 Results and discussions 14 Annual energy simulations -When reflectance increased form 0.1 to 0.7 the annual energy was decreased. for 5 floors: % down 15 floors: % down 30 floors: % down -It was revealed that the wall was larger, the annual energy was more affected by reflective facade. Except for the modeled buildings, it is still unclear how the energy benefit is. Multiple regression analysis 15 -The cooling energy is not able to be expressed as a polynomial expression with five variables. -Window ratio strongly affected the cooling energy. Dependent variable Cooling energy demand (kw/m 2 /year) R 2 : Adjusted coefficient Independent variable Coefficient t-value of determination An intercept (-) * means p-value ** is less than 10%. The number of floors (-) 0.54 ** means p-value ** is less than 5%. A floor (m 2 ) ** A floor plan ratio (-) Window ratio (-) ** Solar reflectance (-) ** 88% * ** means p-value is less than 10%. * means p-value is less than 5%.

9 9 Multiple regression analysis 16 -The heating energy is not able to be expressed as a polynomial expression with five variables. -A floor plan ratio affected the heating demand. Dependent variable Heating energy demand (kw/m 2 /year) Independent variable Coefficient t-value Solar reflectance (-) ** An intercept (-) ** The number of floors (-) ** A floor (m 2 ) A floor plan ratio (-) ** Window ratio (-) ** Solar reflectance (-) R 2 : Adjusted coefficient of determination 53% * ** means p-value is less than 10%. * means p-value is less than 5%. Multiple regression analysis 17 -The annual energy is expressed as a polynomial expression with five variables. - Window ratio affected the annual energy demand. Dependent variable Annual energy demand (kw/m 2 /year) Independent variable Coefficient t-value Solar reflectance (-) An intercept (-) ** The number of floors (-) ** A floor (m 2 ) ** A floor plan ratio (-) ** Window ratio (-) ** Solar reflectance (-) ** An intercept (-) ** R 2 : Adjusted coefficient of determination 86% * ** means p-value is less than 10%. * means p-value is less than 5%.

10 10 Multiple regression analysis 18 -The decreased annual energy is the energy amount difference between reflectance (0.1) and another reflectance (0.4 or 0.7). -Solar reflectance affected the decreased annual energy demand. Dependent variable The decreased annual energy demand (kw/m 2 /year) Independent variable Coefficient t-value Solar reflectance (-) ** An intercept (-) ** The number of floors (-) ** A floor (m 2 ) ** A floor plan ratio (-) Window ratio (-) ** Solar reflectance (-) ** R 2 : Adjusted coefficient of determination 80% * ** means p-value is less than 10%. * means p-value is less than 5%. Multiple regression analysis 19 u The annual energy demand was expressed by a polynomial. -The Annual energy demand = N A The Annual energy demand difference caused by reflectance difference =,, The products difference impact The aged reflectance impact u The designer can estimate energy benefit. u Those functions are limited to the simulated conditions.

11 11 Conclusion Though simulation work 20 1 Heat flux could be controlled by solar reflectance. 2 Annual energy could be also controlled by solar reflectance. 3 When solar reflectance is increased from 0.1 to 0.7, the annual energy is decreased by % Conclusion Through analysis work 21 1 A polynomial with five parameters is proposed to estimate energy benefit. 2 It may be possible to create similar functions through the same procedure. Further works - Facade details variation (U-value, window properties ) - Lighting energy - Energy details (each floor, each direction )

12 12 Additional information u Another my message - Highly reflective facade may be useful in Northern countries. 22 An office room (h=4 m) with a exterior wall (7 x 4m, others: adiabatic) with a window (3 x 2m ) No. Facade surface Emissivity Reflectance Wall Window Scenarios Results Annual U value Cooling Heating Annual energy Wether energy energy energy reduction data (kw/m 2 ) (kw/m 2 ) (kw/m 2 ) (kw/m 2 ) Tokyo, Japan Oslo, Norway Thank you for your attention! mail to: Ihara.takeshi@takenaka.co.jp