My Doctorate Research thesis at ETH Zürich's Institute of Technology in Architecture

1. The Lean Building
ETH Zürich D-ARCH ITA
Clayton C. Miller

2. Context: Major Goals of Built
Environment
• Safety and Comfort of Performance of
Design and
Occupants
Reality often
• Cost Effective Functionality diverge!
• Efficient Use of Resources
• Aesthetically Pleasing
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3. Performance Mismatch
Phenomenon
Building EQ Study - “...usually there is no continuous
2008-2010 European evaluation of the building performance
in order to reach or maintain an
Commission energy-efficient operation.”
(Nuemann and Jacob, 2010)
Building Commissioning - 2004 Survey of 664 buildings
& 2009 Study by Lawrence showing 10,000+ energy
Berkeley National Labs related issues in buildings with
(Mills, 2004; Mills, 2009) 13-16% average energy waste
Numerous Design vs. Most extreme case: 2:1
Operation Case Studies discrepancy between
(Norford et al. 1996; Scofield 2002; Piette et al. 2001; Persson measured and predicted
2005; Kunz et al. 2009)
performance
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4. LEED Certified Buildings Study
by New Buildings Institute
(Turner et. al 2008)
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5. The Data Gap
Time Series
Performance Data
Device/ Simulation BMS/EMS
Zone /BIM Data
System
Rating Utility Calibrated
Building Systems Bills Models
City Life
Cycle
Design Construction Operation Renewal Phase
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6. Proposed General Research
Questions
What performance metrics exist or
can be developed that close the data gap
between the life cycle phases?
How do humans use these metrics?
What novel data science approaches can
be tested to analyze comparisons in a
robust way?
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7. General Areas of Investigation
Useable metrics Building Performance Metrics
applicable to all built upon the “Lean” workflow and
building life cycle the Energy Performance Comparison
phases Methodology
Robust analytics
methods capable of Data Mining Approaches
finding value despite applied to these Metrics
noise and uncertainty
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8. Inspiration: The Lean Movements
Manufacturing Web Development
(Ries 2011) and (Liker et. al 2011)
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9. The Lean Movements
Continuous Improvement
Efficiency
Reduction of Waste
Quality Control
Data-driven Decision Making
Diagram from The Lean Startup by Eric Ries
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10. Metrics Study
Time Series
Performance Data
Device/ Simulation BMS/EMS
Zone /BIM Data
System Lean System Performance Metrics
Rating Utility Calibrated
Building Systems Bills Models
City Life
Cycle
Design Construction Operation Renewal Phase
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11. Performance Metrics
Investigate:
- Current Utilization in the Industry
- Trainability for both Designers and Operators
- Effective Integration into Simulation and Measurement
- Cost effectiveness and efficacy
- Application to data mining approaches
Diagram from: (Friedman et. al. 2011)
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12. Goal
Quality Lean Operations
Device/ with “Kaizen” Events
Control
Zone
System Lean System Performance Metrics
Building Design Phase
Insight and
Understandable
City Intent Feedback!
Design Construction Operation Renewal
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13. Data Mining Research
Approach
Lean System Performance Metrics
Modelica/ Collected Building and
Energyplus Energy Management System
Simulation Data
Novel Application of Time Series Data Pattern/
Shape Recognition Approaches
Life
Cycle
Design Construction Operation Renewal Phase
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14. Conclusion
We are drowning in
information and starving
for knowledge.
- Rutherford D. Roger
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15. My Background
2002-2007
Masters of Architectural Engineering
University of Nebraska Building Systems Design
2006-2008
Mechanical Engineer
Leo A Daly Co. Building Operations
2008-2009
Energy Engineer
Analytics
Sensus Machine Intelligence
Software Development/
2009-2010
Fulbright Scholar/MSc (Building) Management
National University of Singapore
2010-2012 Performance Modeling
Chief Technology Officer Research & Development
Optiras Pte Ltd
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16. Personal Motivations
Strong desire to transform
Building Systems Design the building industry in a
positive way
Building Operations
Analytics Felt the pain of data
Software Development/ analysis in multiple
Management subdomains
Performance Modeling Curiosity of the human-
Research & Development focused aspects of the
building industry
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17. References
Friedman, H., Crowe, E., Sibley, E. Effinger, M. (2011) Building Performance Tracking Handbook, Prepared by Portland Energy Conservation, Inc. Developed for
California Energy Commissioning Collaborative, April 2011(http://www.cacx.org/PIER/documents/bpt-handbook.pdf)
Duda R.O., P.E. Hart, and D.G. Stork, (2001). Pattern Classification, 2nd Ed., John Wiley & Sons, New York, NY.
Liker, J. and Convis, J. (2011). The Toyota Way to Lean Leadership: Achieving and Sustaining Excellence through Leadership Development. 1st Edition. McGraw
Hill
Maile, T. (2010). Comparing Measured and Simulated Building Energy Performance Data. PhD Thesis, Department of Civil and Environmental Engineering,
Stanford University, Stanford, CA
Mills, E. (2011). Building Commissioning: A Golden Opportunity for Reducing Energy Costs and Greenhouse Gas Emissions in the United States. Energy
Efficiency,Volume 4, Issue 2, pp.145-173.
Neumann, C. and Jacob, D. (2010). Results of the project: Building EQ Tools and methods for linking EPBD and continuous commissioning. European
Commission in the programme Intelligent Energy – Europe (IEE)
Norford, L.K., Socolow, R. H., Hsieh, E. S., Spadaro, G.V. (1994). Two-to-one discrepancy between measured and predicted performance of a
‘lowenergy’ office building: insights from a reconciliation based on the DOE-2 model, Energy and Buildings.21(2). 1994, Pages 121-131.
Reddy, T.A., (2006). Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools.
ASHRAE Transactions, 112(1), pp.226-240
Ries, E. (2011). The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Crown Business
Publishing.
Turner, C. and Frankel, M., (2008). Energy Performance of LEED for New Construction Buildings—Final Report, New Buildings Institute, White Salmon, WA,
2008.
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