Deriving archetype templates for urban building energy models based on measured monthly ...

78 Pages·2015·10.21 MB·English

Deriving Archetype Templates for Urban Building
Energy Models Based on Measured Monthly Energy Use
by AR(HIVE
MASSACHUSETTS INSTITUTE
Julia A. Sokol
OF'rECHNOLOLGY
B.A., Mechanical Engineering JUL 3 0 2015
Harvard University, 2010
LIBRARIES
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements
for the degree of Master of Science in Mechanical Engineering
at the
Massachusetts Institute of Technology
June 2015
@ Massachusetts Institute of Technology, 2015. All rights reserved.
Signature redacted
Signature of Author ............................
Departm nt of Mechanical Engineering
May 27, 2015
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Certified by .............. .....................
Christoph R0Ti5Tiart
Associate Professor
Thesis Supervisor
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Certified by .............
C/ Leslie Norford
Professor
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Reader
Accepted by .........................................................................
David Hardt
Professor of Mechanical Engineering
Chairman, Department Committee on Graduate Theses
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Deriving Archetype Templates for Urban Building Energy Models Based on Measured
Monthly Energy Use
by Julia A. Sokol
Submitted to the Department of Mechanical Engineering
on May 27, 2015 in partial fulfillment of the requirements
for the degree of Master of Science in Mechanical Engineering.
Abstract
Interest in urban energy modeling has grown among planners and policy-makers as more and more mu-
nicipalities set targets for reduction of greenhouse gas emissions. Urban-scale building energy models
can help evaluate the efficiency of proposed district designs, consequences of building retrofit inter-
ventions, or energy supply options. Bottom-up models based on physical descriptions and engineering
calculations are the most versatile for modeling scenarios and evaluating results at high spatial and
temporal resolutions. Such urban building energy models (UBEMs) are typically created by grouping
buildings with similar properties into archetypes, which standardize many properties that are not
uniform in reality, such as occupancy-driven parameters. Since most UBEMs are validated using
aggregated, annual measured data, this standardization is usually adequate; however, for a more ac-
curate model that considers end-use differentiation or seasonal variation, neither this standardization
nor this validation method are sufficient.
This work proposes a new methodology for archetype definition and customization using metered
monthly energy data. Customization is done by inferring certain parameters from the energy data
and estimating others probabilistically from parametric analysis. The methodology is developed and
tested on a case study of 453 low-rise residential buildings in Cambridge, Massachusetts. Four model
iterations are compared: single template, eight archetype templates, eight archetypes with individual
building customization, and the latter with the addition of parametric analysis and generation of
frequency distributions for unknown parameters. The results show an improvement in mean goodness
of fit from 46% with one template and 37% with eight templates to 18% for the final iteration. The
distribution of energy use intensities, as well as monthly electricity and gas profiles, approach observed
values closer with each iteration. The results also demonstrate that error metrics based on aggregated
annual consumption, commonly used for urban model validation, are not necessarily representative of
the model's fit on a monthly basis.
Thesis Supervisor: Christoph Reinhart
Title: Associate Professor of Building Technology
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Acknowledgements
I would like to express my deepest gratitude to my research advisor Christoph Reinhart for his
guidance, patience, kindness, and inspiration. I am also grateful to Professors Les Norford and Leon
Glicksman, the rest of the Building Technology Faculty, and Kathleen Ross, for their help and advice,
and for creating a supportive BT community.
This research is part of a larger series of urban modeling projects in the Sustainable Design Lab and
was performed in close collaboration with PhD candidate Carlos Cerezo Davila, whose insight and
experience was invaluable.
To all the talented Building Technology students who have shared their time and knowledge, both in
lab and outside of it-especially Aiko, Cody, David, Irmak, Jeff, Leo, Madeline, Manos, Tarek, and
Timur-you have made the past two years memorable. Thank you to all my other friends at MIT
and farther away (especially Katie and the rest of the EBM lab) and to my dear roommate Polina for
their love and encouragement. Thanks also to Meder, through whom I got to know MIT long before
I could ever imagine being a student here, and to Anton, whose spirit is still here in Cambridge.
Lastly, deepest of thanks to my dad, sister, and brother-in-law for their endless support. And to
Cal-without whom I wouldn't be here-for all the magic.
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Contents
Abstract 3
Acknowledgements 5
Contents 7
List of Figures 10
List of Tables 11
1 Introduction 12
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 15
1.3 Thesis Goals and Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Background 18
2.1 Energy Modeling of Existing Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Calibration of Building Energy Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.1 Calibration Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.2 Calibration Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Urban Building Energy Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.1 Archetype Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.2 User Behavior Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.3 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 Proposed Methodology for Urban Building Energy Modeling 28
3.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.1 Weather Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.1.2 Energy Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Building Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Geometric Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.2 Non-Geometric Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Template Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Template Customization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7
Contents
3.4.1 Inference of Parameters . . . . . . . . . .
3.4.2 Probabilistic Estimation of Parameters . .
3.5 Model Execution . . . . . . . . . . . . . . . . . .
3.6 Model Validation . . . . . . . . . . . . . . . . . .
4 Application of Methodology to Cambridge, MA Case Study
4.1 Data Collection . . . . . . . . . . . . . . . . . . .
4.1.1 Weather Data . . . . . . . . . . . . . . . .
4.1.2 Energy Data . . . . . . . . . . . . . . . .
4.2 Building Data . . . . . . . . . . . . . . . . . . . .
4.2.1 Geometric Properties . . . . . . . . . . .
4.2.2 Non-Geometric Properties . . . . . . . . .
4.3 Template Generation . . . . . . . . . . . . . . . .
4.3.1 Constructions . . . . . . . . . . . . . . . .
4.3.2 Internal Loads and DHW . . . . . . . . .
4.3.3 Schedules . . . . . . . . . . . . . . . . . .
4.4 Template Customization . . . . . . . . . . . . . .
4.4.1 Inference of Parameters . . . . . . . . . .
4.4.2 Probabilistic Estimation of Parameters . .
4.5 Model Iterations . . . . . . . . . . . . . . . . . .
5 Results for Cambridge, MA Case Study
5.1 Error M etrics . . . . . . . . . . . . . . . . . . . . . .
5.2 Annual and Monthly Simulation Results . . . . . . .
5.2.1 Monthly Comparison . . . . . . . . . . . . . .
5.2.2 EUI Distribution Comparison . . . . . . . . .
5.3 Parameter Distrib1tinn . . . . . . . . . . . ..
5.3.1 Inferred Parameters . . . . . . . . . . . . . .
5.3.2 Probabilistically-Estimated Parameters . . .
5.4 Results Summary . . . . . . . . . . . . . . . . . . . .
6 Discussion and Conclusion 64
6.1 D iscussion . . . . . . . . . . . . . . . . . . . . . . . . 64
6.1.1 Parameter Uncertainty Reduction . . . . . . 64
6.1.2 Generation of Improved Archetypes..... 65
6.1.3 Evaluating Consequences of Data Availability 65
6.2 Lim itations . . . . . . . . . . . . . . . . . . . . . . . 66
6.2.1 Geometric Limitations . . . . . . . . . . . . . 66
6.2.2 Modeling Simplifications . . . . . . . . . . . . 67
6.2.3 Limitations of Results . . . . . . . . . . . . . 67
6.3 Future Wo rk . . . . . . . . . . . . . . . . . . . . . . 68
6.3.1 Validation . . . . . . . . . . . . . . . . . . . . 68
6.3.2 Methodology Refinement . . . . . . . . . . . 69
6.3.2.1 Sensitivity Analysis . . . . . . . . . 69
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Contents
6.3.2.2 Hourly Energy Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.3.3 Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
A Energy Model Templates 71
A.1 Constructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 71
A.2 Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Bibliography 74
9
List of Figures
1.1 U.S. energy flows in quadrillion Btu, 2014. [1] . . . . . . . . . . . . . . . . . . . . . . . 13
1.2 Historical energy consumption by end-use sector, in quadrillion Btu. [1] . . . . . . . . 14
1.3 Monthly energy consumption by end-use.sector, 2012-2014, in quadrillion Btu. [1] . . . 14
2.1 Techniques used for estimating regional or national residential energy consumption. [2] 23
3.1 Example of residential monthly energy use in Cambridge, MA. . . . . . . . . . . . . . 32
4.1 Tax parcels of the City of Cambridge. . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Histogram by total 2008 EUI, shaded by construction period (pre-1945, 1946-1980,
post-1980). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Histograms by 2008 EUI, separated into gas and electric use intensities. . . . . . . . . 38
4.4 Monthly energy use intensities for low-rise residential buildings in Cambridge. . . . . . 39
4.5 Building geometry generation process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.6 Energy use shaded by age category of building. . . . . . . . . . . . . . . . . . . . . . . 45
5.1 Rendering of the Cambridgeport 3D model. . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 Annual results for baseline run with a single template assigned to all buildings. . . . . 53
5.3 Annual results for Run 1 with initial templates generated from annual data. . . . . . . 54
5.4 Annual results for templates customized to each building based on parameters inferred
from m onthly data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.5 Annual results for lowest-error parametric runs with variable occupancy and setpoints. 56
5.6 Measured monthly gas and electric use intensity. . . . . . . . . . . . . . . . . . . . . . 57
5.7 Monthly simulation results for a subset of 200 buildings. . . . . . . . . . . . . . . . . . 58
5.8 Measured (shaded grey) and simulated (shaded green) EUI distributions for neighbor-
hood, in W/m2: Run 0 (top left), Run 1 (top right), Run 2 (bottom left), Run 3
(bottom right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.9 Distribution of peak electric load intensities. . . . . . . . . . . . . . . . . . . . . . . . . 60
5.10 Distribution of peak domestic hot water flow. . . . . . . . . . . . . . . . . . . . . . . . 60
5.11 Distributions of AC use (left) and heating system efficiencies (right). . . . . . . . . . . 61
5.12 Distribution of occupancy density per square meter for GOF < 10. . . . . . . . . . . . 61
5.13 Cooling (left) and heating (right) setpoint distributions for GOF < 10. . . . . . . . . . 62
5.14 Goodness of fit by individual building for every run. (Note that the vertical axis is
truncated, so the highest error points are not displayed.) . . . . . . . . . . . . . . . . . 63
6.1 3D models of Cambridgeport with and without LiDAR data. . . . . . . . . . . . . . . 67
10