Meer nauwkeurige energiebesparings-voorspellingen voor woningen: een pragmatische BIM-aanpak zowel met als zonder eigen BIM-model

Transcription

1 Meer nauwkeurige energiebesparings-voorspellingen voor woningen: een pragmatische BIM-aanpak zowel met als zonder eigen BIM-model Marc Delghust UGent 20 oktober 2016 Antwerpen

2 Context & challenge real vs. theoretical energy use in houses simplified calculation methods: single or multi-zone

3 old vs. new old housing neighbourhood current standard housing neighbourhood 3

4 real vs. theoretical energy use real gain? 4

5 Temperature take-back: building level Old neighbourhood Standard neighbourhood 5

6 Temperature take-back: room level Old neighbourhood Standard neighbourhood 6

7 EPB/EPC: simplified models Multi-Zone Single-Zone θ Dynamic θ Quasi-steady-state t t 7

8 extended model: multi-zone Multi-Zone Single-Zone θ Quasi-steady-state t 8

9 example: EPB 9

10 example: multi-zone

11 example: single-zone with correction factors NEN 7120: intermittency & spatial reduction

12 example: temperatures at building and room level

13 example: comparing energy saving measures Loft insulation Double glazing Cavity wall ins. Floor insulation

14 example: comparing energy saving measures single-zone bias Loft insulation overestimated savings Double glazing Cavity wall ins. underestimated savings Floor insulation

15 Algorithm feeding approach BIM, with and without BIM model

16 BIM: specific house 16

17 BIM: parametric typology 17

18 parametric typologies, multi-zone case, single-zone selection fitting multi-zone 18

19 parametric typologies, multi-zone building stock, single-zone multiple selections multiple fittings multi-zone 19

20 Implentation BIM based simulation tool: selection fitting Revit-plug-in (C#,.NET) gbxml format Excel-interface: o Inputs: parameters for fitting parameters for scenario analysis (e.g. user profiles) o Outputs: geometric and physical characteristics simulation results Calculation kernel: o official single-zone EPB o multi-zone quasi-steady-state 20

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22 Excel inputs Automatic geometry parametrization and calculation outputs: (EPB & more realistic multi-zone model) 22

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24 Type (Dutch / English) Run query Manual selection 24

25 Replacement models vs. original models Residential building stock single zone Individual single-family houses multi zone

26 Quality of replacement models parametric typologies vs. EPB-database single-zone as-is & case studies multi-zone scenarios

27 Building stock: single-zone geometry 5000 Nearly perfect fit on geometrical properties from set of geometric equations

28 Building stock: single-zone simulation Very good fit on building stock level vs. Varying deviations on level of individual case Compensation of over- and underestimations on building stock level. ~orientation 28

29 Case-studies: multi-zone simulations 29

30 Case-studies: multi-zone simulations 30

31 Application & findings BIM, with and without BIM model User friendly workflow

32 Application: education Models as tools to experiment and understand standardised EPB calculation vs. realistic calculation consequences of different heating profiles consequences of different typologies resulting in different cost optimal solution: no one solution fits all requirements: CO2-reduction vs. cost efficiency? 32

33 Application: education design Models as tools to experiment and understand standardised EPB calculation vs. realistic calculation consequences of different heating profiles consequences of different typologies resulting in different cost optimal solution: no one solution fits all requirements: CO2-reduction vs. cost efficiency? 33

34 Application: policy support, building stock Consider real distributions Comparing different calculation methods S-peil : definition, requirements and consequences for different typologies and energy saving measures APP 1 GESLOTEN 3 / APP 2 HALFOPEN 3 VRIJSTAAND 1 VRIJSTAAND 3 34

35 Application: policy support, building stock What if we would consider changing heating profiles? -100% -80% -60% -40% -20% 0% 100% 75% 50% roof ins._δabs + c.h. 2015_Δabs + c.h. PH_Δabs + lowt roof ins._δ% + c.h. Δabs 25% 0% 2015_Δ% + c.h. PH_Δ% + lowt Qh_FINAL_EPB [kwh/(m².yr)] Δ%

36 Application: policy support, building stock What if we would consider changing heating profiles? -100% -80% -60% -40% -20% 0% 100% 75% 50% roof ins.-prof2_δabs + c.h prof2_Δabs + c.h. PH-prof1_Δabs + lowt roof ins.-prof2_δ% + c.h. Δabs 25% 2015-prof2_Δ% + c.h. PH-prof1_Δ% + lowt Δ% 0% Qh_FINAL_MZ [kwh/(m².yr)]

37 Conclusions more realistic models usable for large scale building stock research ànd small residential housing projects power of BIM not only for BIM-users models are just tools (=> effort & result) integration of different models = more for less know and understand their assumptions

38 Meer informatie? 32 (0) Sint Pietersnieuwstraat 41 Blok 4, 9000 Gent