Automated diagnostics and analytics for buildings

640 Pages·2015·137.178 MB·English

by  Brambley, Michael R., Capehart, Barney L

Automated Diagnostics and
Analytics for Buildings

Automated Diagnostics and
Analytics for Buildings
Editors
Barney L Capehart PhD, CEM
and
Michael R Brambley, PhD
Library of Congress Cataloging-in-Publication Data
Capehart, Barney L.
Automated Diagnostics and Analytics for Buildings/ Barney Capehart.
pages cm
Includes bibliographical references and index.
ISBN 0-88173-732-1 (alk. paper) -- ISBN 0-88173-733-X (electronic) -- ISBN 978-1-4987-0611-7
(Taylor & Francis distribution : alk. paper)
Library of Congress Control Number: 2014946700

Automated Diagnostics and Analytics for Buildings by Barney Capehart and Michael R. Brambley
©2015 by The Fairmont Press, Inc. All rights reserved. No part of this publication may be repro-
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Contents
SECTION I
The Case for Automated Tools for Facility Management and Operation
Chapter 1
Introduction 3
The Energy and Sustainability Connections . . . . . . . . . . . . . . . . . . . . . . . . 3
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Chapter 2
Energy-Smart Buildings Demonstrating How Information
Technology Can Cut Energy Use and Costs of Real Estate Portfolios 9
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Introduction: The Case for Smart Buildings . . . . . . . . . . . . . . . . . . . . . . . 10
Case study—Smart Buildings at Microsoft . . . . . . . . . . . . . . . . . . . . . . . . 13
Future smart Building Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Chapter 3
Perspective on Building Analytics Adoption 23
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Building Energy Analytics Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Building Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
People Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Chapter 4
Delivering Successful Results from Automated Diagnostics Solutions 29
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Skill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Quality of the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Current State or Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Delivery Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Chapter 5
Intelligent Efficiency: Opportunities, Barriers, and Solutions 35
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Intelligent Efficiency Defined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
A “Systems Approach” to Energy Savings . . . . . . . . . . . . . . . . . . . . . . . . 38
v
Eliminating the Degradation of Energy Savings . . . . . . . . . . . . . . . . . . . . 39
Energy Savings from Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Non-Energy Benefits: Safety, Quality, Job Creation . . . . . . . . . . . . . . . . . . . 43
Harvesting the Potential of Building Automation Systems . . . . . . . . . . . . . . 44
Smart Manufacturing and Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . 48
Energy Measures Considered in the Economic Analysis . . . . . . . . . . . . . . . . 49
Distinguishing Intelligent Efficiency Measures . . . . . . . . . . . . . . . . . . . . . 50
Intelligent Energy Measures Included in the Economic Analysis . . . . . . . . . . . 51
Intelligent Efficiency Measures for the Commercial Sector . . . . . . . . . . . . . . 51
Accounting for Interactions Between Energy Measures in the
Commercial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Intelligent Efficiency Measures for the Industrial Sector . . . . . . . . . . . . . . . . 55
Plant Automation and Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Industrial Building Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Results: Cumulative Potential Energy Savings from Intelligent Efficiency . . . . . . 57
The Challenges of Big Data and Data Analytics . . . . . . . . . . . . . . . . . . . . . 58
A Common Interconnection to Communicate Energy Data . . . . . . . . . . . . . . 60
Common Protocols for Determining Energy Savings . . . . . . . . . . . . . . . . . . 61
Utility Sector Efficiency Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Challenges Facing Efficiency Programs . . . . . . . . . . . . . . . . . . . . . . . . . 63
Including Automation and Controls in Efficiency Programs . . . . . . . . . . . . . 64
Challenges of Incorporating Automation and Controls into Efficiency Programs . . 64
Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
SECTION II
Current Technology, Tools, Products, Services, and Applications
Chapter 6
Automated Diagnostics 75
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Don’t Generate False Alarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
Focus on Problems the Customer Cares About . . . . . . . . . . . . . . . . . . . . . 77
Use Hierarchical Fault Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Present Information, Not Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Advanced Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Chapter 7
Enabling Efficient Building Energy Management through
Automated Fault Detection and Diagnostics 83
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Harvesting Data and the Importance of Reliable Data Storage . . . . . . . . . . . . 83
Data Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Asset Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Operational Guidelines™ and the Low-Hanging Fruit . . . . . . . . . . . . . . . . . 85
Automated Fault Detection and Diagnostics . . . . . . . . . . . . . . . . . . . . . . 86
Chapter Summary/Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100
About Ezenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100
vi
Chapter 8
A Practical, Proven Approach to Energy Intelligence,
Measurement, and Management 101
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101
Automated Diagnostics as a Component of Energy Intelligence Software . . . . . .102
Case Study: The Power of Efficiency-SMART Insight’s
Meter-Level Analytics at Beacon Capital . . . . . . . . . . . . . . . . . . . . . .102
Case Study: Mining Operational Savings from Building Management
Systems with EfficiencySMART at Western Connecticut State University . . .105
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107
Chapter 9
Di-BOSS: Research, Development & Deployment of the World’s First
Digital Building Operating System 109
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109
The Origin of Di-BOSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
The Di-BOSS Building Operating System Solution . . . . . . . . . . . . . . . . . . . 113
Di-BOSS Tenant Cockpit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
TPO, the Brain of Di-BOSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Real-Time Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Preheat, Startup, and Ramp-down Recommendations . . . . . . . . . . . . . . . . . 118
Convergence to the Operational Optimal . . . . . . . . . . . . . . . . . . . . . . . .120
Now-Cast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .122
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .124
Chapter 10
Analytics, Alarms, Analysis, Fault Detection and Diagnostics 127
Alarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127
FDD—Fault Detection & Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . .128
Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .128
Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129
Some Examples Help to Highlight the Distinctions . . . . . . . . . . . . . . . . . . .130
Data Access—A Key Requirement for all Data-oriented Tools . . . . . . . . . . . . .132
Analytics as an Exploratory Process . . . . . . . . . . . . . . . . . . . . . . . . . . .132
A Fast Moving Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135
Chapter 11
Using Custom Programs to Enhance Building Diagnostics and Energy Savings 137
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137
The Facility Time Schedule Program . . . . . . . . . . . . . . . . . . . . . . . . . . .137
The Building Tune-Up System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138
The Carrier Alarm Notification Program . . . . . . . . . . . . . . . . . . . . . . . . .139
Ems Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146
Chapter 12
88 Acres—How Microsoft Quietly Built the City of the Future 149
The Visionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149
88 Acres in a One-Stoplight Town . . . . . . . . . . . . . . . . . . . . . . . . . . . . .150
The Living, Breathing Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151
vii
Enough Data to Change the World . . . . . . . . . . . . . . . . . . . . . . . . . . . .152
A Smarter Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153
Chapter 13
Improving Airport Energy Efficiency through Continuous Commissioning®
Process Implementation at Dallas/Fort Worth International Airport 155
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .155
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .155
The Continuous Commissioning® Process . . . . . . . . . . . . . . . . . . . . . . . .156
The CC Process at DFW International Airport Facilities . . . . . . . . . . . . . . . .157
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .160
Chapter 14
ECAM+ A Tool for Analyzing Building Data to Improve and
Track Performance 163
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .163
Credits for ECAM+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .163
ECAM+ Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .163
ECAM+ Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165
Chart Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .166
Measurement and Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .168
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .174
Chapter 15
Outside The Box: Integrating People & Controls
To Create a Unique Energy Management Program—A Caltech Case Study 175
The Business Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175
Optimal Performance Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .176
The Human Element of Automated Diagnostics . . . . . . . . . . . . . . . . . . . .177
Transition to Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .177
AEM from the Solutions Provider’s Perspective . . . . . . . . . . . . . . . . . . . .178
Next Steps for the AEM Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178
Chapter 16
Value Proposition of FDD Solutions 179
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .179
Introduction—Why FDD and Why Now? . . . . . . . . . . . . . . . . . . . . . . . .179
Analysis—Leveraging Value from FDD approach . . . . . . . . . . . . . . . . . . .180
First, Use What You Have . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .181
Solutions—Finding the Right Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . .183
Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .184
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .185
Chapter 17
FDD: How High Are the Market Barriers to Entry? 187
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187
Fault Detection and Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187
Who . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188
What . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189
When . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189
viii
Where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .190
Why . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191
How . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .192
Chapter 18
Diagnostics for Monitoring-based Commissioning 193
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .193
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .193
SECTION III
Methodology and Future Technology
Chapter 19
Model-based Real-time Whole Building Energy Performance
Monitoring and Diagnostics 205
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .205
System Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .206
Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .212
Conclusions and Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . . . . . .223
Chapter 20
Advanced Methods for Whole Building Energy Analysis 227
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .227
Technical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .228
Related Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .228
Calculation and Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .228
Application Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .230
Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .230
Technical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .232
Related Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .232
Calculation and Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .232
Application Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .232
Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .234
Technical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .234
Related Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .234
Calculation and Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .234
Application Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .238
Identifying Abnormal Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . .238
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .240
Chapter 21
Identification of Energy Efficiency Opportunities Through Building Data
Analysis and Achieving Energy Savings through Improved Controls 241
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241
Analyzing Interval Meter Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241
Analyzing Building Automation
System Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .253
Conclusions/Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .269
ix