Image Credit: SeeGlasgow www.strath.ac.uk/esru www.strath.ac.uk/esru esru@strath.ac.uk Slide 1
Workshop on Potential Technological Developments for Zero-carbon Buildings � � � � � � � � � Energy modelling, experimentation and real building assessment Paul Strachan ESRU, University of Strathclyde ,Glasgow, Scotland paul@esru.strath.ac.uk Hong Kong, 17 October 2013 www.strath.ac.uk/esru Slide 2
Contents Need for dynamic modelling Difficulties of dynamic modelling Methodology for component-level testing Development and validation of simulation programs Need for more research: combined monitoring and modelling www.strath.ac.uk/esru Slide 3
European Standard: EN13790:2008 Energy balance for heating mode - building www.strath.ac.uk/esru Slide 4
European Standard: EN13790:2008 Energy balance for heating mode - system www.strath.ac.uk/esru Slide 5
Energy-related building technologies Smart facades Passive solar energy Ventilation systems www.strath.ac.uk/esru Slide 6
Energy-related building technologies Building-integrated renewables Heating systems Cooling systems Micro-cogeneration Electrical demand control Smart metering www.strath.ac.uk/esru Slide 7
Need for dynamic thermal modelling Design – new buildings or major refurbishments Building compliance and asset certification Buildings with complex operations or complex interactions (… and buildings are getting more complex …) Large range of new passive and active technologies – how do we choose the most appropriate solution? Assessment of integrated performance – thermal comfort, lighting comfort, IAQ …, as well as energy Scaling performance of new technologies from component scale to building scale Test robustness against future climate change Use information from performance in practice to improve reliability of predictions (particularly user behaviour) www.strath.ac.uk/esru Slide 8
Simulation program capabilities basement model LW radiation solar shading and ground model tracking lighting occupancy n-D conduction non-linear material properties life cycle analysis moisture flow building phase change mould growth user behaviour materials prediction simulation materials databases electrical systems glazing systems embodied energy CHP HVAC variable renewables fluid flow convection acoustics algorithms heating and cooling systems zonal air flow CFD control simulation based advanced control control algorithms www.strath.ac.uk/esru Slide 9
Difficulty of modelling building energy systems Complexity is due to: dynamic effects with varying time constants interaction between various heat transfer flow paths non-linear effects stochastic processes e.g for user behaviour models www.strath.ac.uk/esru Slide 10
Do programs predict performance accurately? Many validation studies over decades of research in BESTEST MZ Conduction – Sensible Cooling Load international projects Usually good agreement with analytical solutions for simple cases, with inter ‐ program comparisons, and with well ‐ controlled small ‐ scale experiments on test rooms BESTEST Internal Windows – Sensible Cooling Load www.strath.ac.uk/esru Slide 11
But … mismatch between predictions and measurements “In theory, theory and practice are the same. In practice, they are not.” ― Albert Einstein Many studies show that the actual performance of the constructed building may deviate significantly from the theoretically designed performance … although Post- Occupancy Evaluation studies are still not common … www.strath.ac.uk/esru Slide 12
Measured versus predicted whole house heat losses (W/K) for new build dwellings in the UK Co ‐ heating tests: Leeds Metropolitan University http://www.leedsmet.ac.uk/as/cebe/projects/coheating_test_protocol.pdf www.strath.ac.uk/esru Slide 13
Energy Performance of LEED for New Construction Buildings 2008 Measured versus Proposed Savings Measured/Design Ratios Relative Percentages to Design EUI Cathy Turner and Mark Frankel, New Buildings Institute www.strath.ac.uk/esru Slide 14
Reasons for mismatch • Errors in programs (“bugs”) internal • Simplifications and assumptions in mathematical models errors • Factors in construction, operation and use of the building • Effects of user behaviour on performance during use • Effects of mismatch between design and construction (late design changes) • Poor workmanship (thermal bridges, air leakage …) external • Poor commissioning, operation and maintenance of building, errors systems and controls • Allowances for energy used for lifts, hot water systems, external lighting … • Measurement error (e.g. discrepancies between bills and meter readings; meter errors …) • Differences between weather used in predictions and actual weather • Modeller error www.strath.ac.uk/esru Slide 15
How to resolve differences Are differences due to incorrect assumptions in the design, to deficiencies in the modelling programs, or to poor commissioning/operation? We need to improve our confidence in modelling predictions through calibration and validation techniques We need to know more about building performance in practice from detailed monitoring studies Better understanding of building physics – particularly convection including uncertainty analysis Better data on dynamic performance of systems Better understanding of influence of user behaviour Better modelling of realistic control www.strath.ac.uk/esru Slide 16
Component Evaluation Procedure Laboratory Experiments e.g. measurement of thermophysical and optical properties (Outdoor) Test Cell Experiments - high quality data sets Simulation Model Calibration System Identification - check on component-level - analysis to determine key modelling performance indicators Full-scale Building Modelling - determine building energy and environmental performance with component integration Real Building Monitoring - performance in practice www.strath.ac.uk/esru Slide 17
Case Studies from EC Projects www.strath.ac.uk/esru Slide 18
Dynamic Testing using Outdoor Test Cells … www.strath.ac.uk/esru Slide 19
Double ‐ skin Facade Testing: Aalborg Outdoor test cell and operational modes ‐ buffer(left) and external air curtain (right) www.strath.ac.uk/esru Slide 20
Double ‐ skin Facade Testing: Aalborg 1000.0 900.0 ESP-r Heat losses, W 800.0 BSim 700.0 VA1 1 4 600.0 TRNSYS_TUD 500.0 Experimental 400.0 300.0 Total heat losses from the experiment room www.strath.ac.uk/esru Slide 21
Double ‐ skin Facade Testing: Aalborg Air temperatures and cooling loads in experiment room www.strath.ac.uk/esru Slide 22
Double ‐ skin Facade Testing: Aalborg Hourly averaged mass flow rate in the DSF cavity, measured with the velocity profile method at h=1.91m www.strath.ac.uk/esru Slide 23
General conclusions drawn from IEA, EC and other validation studies • Modelling uncertainties (e.g. convection) • Outdoor test cell experiments Representative of real buildings? • Resources Time consuming and expensive • Lack of high-quality empirical datasets, especially at whole building level • Majority of studies involve inter-program comparison, not empirical validation • BESTEST Reference ranges out-of-date • Complex models e.g. smoke propagation, moisture modelling: large variations in predictions. • Commercial programs Some commonly used programs haven't been participants in IEA validation studies (IESVE, Equest, TAS ...). • New technologies . Continuous need for new tests (phase change materials, multifoil insulation, …) www.strath.ac.uk/esru Slide 24
Annex 58 IEA EBC Annex 58 Reliable building energy performance characterisation based on full scale dynamic measurements Objectives ‐ Determine the actual energy performance of buildings ‐ Characterise the dynamic behaviour of buildings (grey box models) ‐ Validate our numerical BES ‐ models ‐ Guarantee quality of measurements / data analysis / use of the results www.strath.ac.uk/esru Slide 25
International workshop in 2011 gave an overview of existing full scale test facilities 26 www.strath.ac.uk/esru Slide 26
Full scale testing requires quality! Experimental set ‐ up Test infrastructure Use of results Data analysis 27 www.strath.ac.uk/esru Slide 27
Structure of Annex 58 Annex 58 Collection and evaluation of in situ activities Subtask 1 Experimental set ‐ up Test infrastructure Use of results Data analysis Subtask 2 Subtask 3 Network of Excellence Subtask 5 Application of developed concepts Subtask 4 www.strath.ac.uk/esru Slide 28
Activities Annex 58 Global framework: Subtask 1. State of the art on full scale testing and dynamic data analysis round robin experiment Subtask 2. Optimising full scale dynamic testing Subtask 3. Dynamic data analysis and performance characterisation Case study 2 Case study 3 Subtask 4. Application of the developed framework Subtask 5. Network of excellence www.strath.ac.uk/esru Slide 29
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