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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

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www.strath.ac.uk/esru Slide 1 esru@strath.ac.uk www.strath.ac.uk/esru

Image Credit: SeeGlasgow

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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

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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

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European Standard: EN13790:2008 Energy balance for heating mode - building

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European Standard: EN13790:2008 Energy balance for heating mode - system

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Energy-related building technologies

Smart facades Passive solar energy Ventilation systems

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Energy-related building technologies

Building-integrated renewables Heating systems Cooling systems Micro-cogeneration Electrical demand control Smart metering

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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)

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Simulation program capabilities

building simulation

lighting moisture flow n-D conduction ground model

basement model mould growth prediction

control

simulation based control advanced control algorithms

zonal air flow CFD

variable convection algorithms non-linear material properties phase change materials

  • ccupancy

solar shading and tracking LW radiation electrical systems

CHP

HVAC

renewables fluid flow

materials databases

embodied energy

acoustics glazing systems life cycle analysis user behaviour

heating and cooling systems

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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

Difficulty of modelling building energy systems

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Do programs predict performance accurately?

Many validation studies over decades of research in 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 BESTEST MZ Conduction – Sensible Cooling Load

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But … mismatch between predictions and measurements

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 …

“In theory, theory and practice are the same. In practice, they are not.” ― Albert Einstein

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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

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Energy Performance of LEED for New Construction Buildings 2008

Measured/Design Ratios Relative to Design EUI Measured versus Proposed Savings Percentages

Cathy Turner and Mark Frankel, New Buildings Institute

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Reasons for mismatch

internal errors external errors

  • Errors in programs (“bugs”)
  • Simplifications and assumptions in mathematical models
  • 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 …)
  • Poor commissioning, operation and maintenance of building,

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
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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

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Component Evaluation Procedure

Laboratory Experiments e.g. measurement of thermophysical and optical properties (Outdoor) Test Cell Experiments

  • high quality data sets

System Identification

  • analysis to determine key

performance indicators Simulation Model Calibration

  • check on component-level

modelling Full-scale Building Modelling

  • determine building energy and environmental performance with

component integration Real Building Monitoring

  • performance in practice
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Case Studies from EC Projects

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Dynamic Testing using Outdoor Test Cells …

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Double‐skin Facade Testing: Aalborg

Outdoor test cell and operational modes ‐ buffer(left) and external air curtain (right)

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Double‐skin Facade Testing: Aalborg

300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0 Heat losses, W

ESP-r BSim VA1 1 4 TRNSYS_TUD Experimental

Total heat losses from the experiment room

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Double‐skin Facade Testing: Aalborg

Air temperatures and cooling loads in experiment room

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Double‐skin Facade Testing: Aalborg

Hourly averaged mass flow rate in the DSF cavity, measured with the velocity profile method at h=1.91m

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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, …)

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Reliable building energy performance characterisation based on full scale dynamic measurements

Annex 58

‐ 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

IEA EBC Annex 58 Objectives

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International workshop in 2011 gave an overview of existing full scale test facilities 26

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Test infrastructure Experimental set‐up Data analysis Use of results Full scale testing requires quality!

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Network of Excellence

Subtask 5 Subtask 3 Subtask 2

Test infrastructure Experimental set‐up Data analysis Use of results Collection and evaluation of in situ activities

Subtask 1

Application of developed concepts

Subtask 4 Annex 58

Structure of Annex 58

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Global framework:

Annex 58

Subtask 1. State of the art on full scale testing and dynamic data analysis Subtask 2. Optimising full scale dynamic testing Subtask 3. Dynamic data analysis and performance characterisation Subtask 4. Application of the developed framework Subtask 5. Network of excellence

Case study 3 Case study 2 round robin experiment

Activities

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Global framework:

Activities

Annex 58

Subtask 1. State of the art on full scale testing and dynamic data analysis Subtask 2. Optimising full scale dynamic testing Subtask 3. Dynamic data analysis and performance characterisation Subtask 4. Application of the developed framework Subtask 5. Network of excellence

Case study 1 Verification of BES‐models Case study 3

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Subtask 1. State of the art on full scale testing and dynamic data analysis Subtask 2. Optimising full scale dynamic testing Subtask 3. Dynamic data analysis and performance characterisation Subtask 4. Application of the developed framework Subtask 5. Network of excellence

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Global framework: round robin experiment Case study 2 Case study 3

International Commerce Centre (ICC)

Global framework

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Conclusions

The rate of development of modelling capabilities has not kept pace with the range of technical solutions available. Need to improve:

Program functionality: technical developments needed User Interfaces: must be easier to use than currently Interoperability: need BIM to become pervasive User competence: training, education Detailed monitoring: building performance in practice – linked to modelling capability Intelligent analysis: development of analysis tools to suggest the most appropriate technologies to meet the electrical, heating, hot water and cooling demands, rather than evaluate a set of pre-defined designs.

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Image Credit: SeeGlasgow