• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Lean Building Research Introduction
 

Lean Building Research Introduction

on

  • 817 views

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

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

Statistics

Views

Total Views
817
Views on SlideShare
268
Embed Views
549

Actions

Likes
0
Downloads
0
Comments
0

4 Embeds 549

http://leanbuilding.org 384
http://www.leanbuilding.org 130
http://leanbuildingdotorg.wordpress.com 27
http://54.228.233.101 8

Accessibility

Categories

Upload Details

Uploaded via as Apple Keynote

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • \n
  • \n
  • \n
  • \n
  • Through my experiences I have observed the full range means for characterizing performance in buildings. These means can be arranged on a scale of Detail of Data and Building Life Cycle Phase. \n- Most of the approaches were designed with only a single building phase in mind\n- Gaps in not only methods but professions, terminology, and incentives exist between the building phases\n- Performance is rarely verified adequately and feedback to design is mostly nonexistent\n- A few approaches exist in attempts to bridge design and operation\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • Novel System-focused Coefficients of Performance or Metrics\nTypes:\nPure efficiency - COP - kWcooling/kWelectricity\nEfficiency and Conservation focused - kWh/m2/year or kWh/person\n\nSteps:\nCreation of a novel framework of performance coefficients to be used in ALL phases of the building life cycle\n\n\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • \n
  • After an exhaustive literature review of technologies and approaches three scientific steps are to be taken:\n- Review of multiple case studies of existing buildings, construction phase, and buildings in the design process\n- Simulation of selected systems in theory and practice using Energyplus and Modelica\n- Review and selection of novel statistical comparison methods in order to increase comprehensibility, efficacy, usability, cost effectiveness of metrics\n\nMy statistics background:\n- Employed a PhD CMU Physics in my company to develop data mining approaches\n
  • I have personally experienced the massive misuse of effort and resources on instrumentation systems that will probably never be used or understood by those who can use the information the most!!!!\n
  • Four key areas prepare me well for my research:\n- Building Design - understanding the means, methods, goals of architects, engineers and multiple types of building engineers\n- Operations Analytics - I’ve seen firsthand how and why performance intent doesn’t always become reality. What types of collected sensor data exists and the challenges present\n- Software Development - Understanding of scalable software technologies that can enhance\n- Simulation R&D - Insight into the state of the art in forward and data-driven performance modeling\n
  • Four key areas prepare me well for my research:\n- Building Design - understanding the means, methods, goals of architects, engineers and multiple types of building engineers\n- Operations Analytics - I’ve seen firsthand how and why performance intent doesn’t always become reality. What types of collected sensor data exists and the challenges present\n- Software Development - Understanding of scalable software technologies that can enhance\n- Simulation R&D - Insight into the state of the art in forward and data-driven performance modeling\n
  • The innovation is threefold:\n- Metrics designed and standardized for ALL Building Life Cycle\n- Efficacy evaluation\n

Lean Building Research Introduction Lean Building Research Introduction Presentation Transcript

  • The Lean Building ETH Zürich D-ARCH ITA Clayton C. Miller
  • 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 2
  • Performance Mismatch PhenomenonBuilding EQ Study - “...usually there is no continuous2008-2010 European evaluation of the building performance in order to reach or maintain anCommission energy-efficient operation.”(Nuemann and Jacob, 2010)Building Commissioning - 2004 Survey of 664 buildings& 2009 Study by Lawrence showing 10,000+ energyBerkeley National Labs related issues in buildings with(Mills, 2004; Mills, 2009) 13-16% average energy wasteNumerous Design vs. Most extreme case: 2:1Operation Case Studies discrepancy between(Norford et al. 1996; Scofield 2002; Piette et al. 2001; Persson measured and predicted2005; Kunz et al. 2009) performance 3
  • LEED Certified Buildings Study by New Buildings Institute (Turner et. al 2008) 4
  • The Data Gap Time SeriesPerformance DataDevice/ Simulation BMS/EMS Zone /BIM Data System Rating Utility CalibratedBuilding Systems Bills Models City Life Cycle Design Construction Operation Renewal Phase 5
  • Proposed General Research QuestionsWhat performance metrics exist orcan be developed that close the data gapbetween the life cycle phases?How do humans use these metrics?What novel data science approaches canbe tested to analyze comparisons in arobust way? 6
  • General Areas of InvestigationUseable metrics Building Performance Metricsapplicable to all built upon the “Lean” workflow andbuilding life cycle the Energy Performance Comparisonphases MethodologyRobust analyticsmethods capable of Data Mining Approachesfinding value despite applied to these Metricsnoise and uncertainty 7
  • Inspiration: The Lean Movements Manufacturing Web Development (Ries 2011) and (Liker et. al 2011) 8
  • The Lean Movements Continuous ImprovementEfficiency Reduction of WasteQuality Control Data-driven Decision Making Diagram from The Lean Startup by Eric Ries 9
  • Metrics Study Time SeriesPerformance DataDevice/ Simulation BMS/EMS Zone /BIM Data System Lean System Performance Metrics Rating Utility CalibratedBuilding Systems Bills Models City Life Cycle Design Construction Operation Renewal Phase 10
  • Performance MetricsInvestigate:- 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) 11
  • Goal Quality Lean OperationsDevice/ with “Kaizen” Events Control ZoneSystem Lean System Performance MetricsBuilding Design Phase Insight and Understandable City Intent Feedback! Design Construction Operation Renewal 12
  • Data Mining Research Approach Lean System Performance MetricsModelica/ Collected Building andEnergyplus Energy Management SystemSimulation Data Novel Application of Time Series Data Pattern/ Shape Recognition Approaches Life CycleDesign Construction Operation Renewal Phase 13
  • Conclusion We are drowning ininformation and starving for knowledge. - Rutherford D. Roger 14
  • My Background2002-2007Masters of Architectural EngineeringUniversity of Nebraska Building Systems Design2006-2008Mechanical EngineerLeo A Daly Co. Building Operations2008-2009Energy Engineer AnalyticsSensus Machine Intelligence Software Development/2009-2010Fulbright Scholar/MSc (Building) ManagementNational University of Singapore2010-2012 Performance ModelingChief Technology Officer Research & DevelopmentOptiras Pte Ltd 15
  • Personal Motivations Strong desire to transformBuilding Systems Design the building industry in a positive way Building Operations Analytics Felt the pain of dataSoftware Development/ analysis in multiple Management subdomains Performance Modeling Curiosity of the human-Research & Development focused aspects of the building industry 16
  • ReferencesFriedman, H., Crowe, E., Sibley, E. Effinger, M. (2011) Building Performance Tracking Handbook, Prepared by Portland Energy Conservation, Inc. Developed forCalifornia 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. McGrawHillMaile, T. (2010). Comparing Measured and Simulated Building Energy Performance Data. PhD Thesis, Department of Civil and Environmental Engineering,Stanford University, Stanford, CAMills, E. (2011). Building Commissioning: A Golden Opportunity for Reducing Energy Costs and Greenhouse Gas Emissions in the United States. EnergyEfficiency,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. EuropeanCommission 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.226-240Ries, E. (2011). The Lean Startup: How Todays Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Crown BusinessPublishing.Turner, C. and Frankel, M., (2008). Energy Performance of LEED for New Construction Buildings—Final Report, New Buildings Institute, White Salmon, WA,2008. 17