urban informatics: harnessing data to understand socio
TRANSCRIPT
Urban Informatics: Harnessing data to understand socio-technical dynamics in the
urban built environment
Rishee K. JainAsst. Professor of Civil & Env. Eng.
Director, Urban Informatics LabStanford University
Urban Informatics Lab (UIL)
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We analyze data to understand interactions betweenpeople, buildings and energy systems in cities.
Intra-buildingdynamics
Community dynamics(inter-building)
Urban-scaledynamics
Why people, buildings and energy in cities?
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• 75% of world’s energy usage comes from cities
• 70% of city energy usage comes from buildings (Chicago, NYC)
• “Buildings don’t use energy, people do”
A sustainable, smart city must address challenges at the intersection of people,
building and energy systems
Opportunity: emerging data streams
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In-situ sensors Remote sensors Organic data
+43 M smart meters now in the U.S.
NYC generates 1 TB of data every day
Nearly 2 B+ people have smart phones
City of Alexandria
Intra-building dynamics – socio-spatial dynamics of energy usage
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Occupants• Space• Time• Social
Building systems
• Space• Time
Data-driven optimization + design
Lacks co-optimization of occupants + building
systems
Occupancy Energy Signal Processing on Graphs (OESPg) framework
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Time
Space
Social
Sonta, A., Jain, R., Gulbinas, R., Moura, J., Taylor, J. (in press). “OESPG: A Computational Framework for Multidimensional Analysis of Occupant Energy Use Data in Commercial Buildings,” ASCE Journal of Computing in Civil Engineering.
Occupant 1 is “out-of-sync”
Community (inter-building) dynamics
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Credit: microgridinsitute.org
What are community impacts of building
energy usage?
How do we plan for distributed energy
infrastructure amidst socio-technical complexities?
What are the socio-technical and energy
burdens of slum redevelopment?
Data-driven infrastructure planning amidst socio-technical complexities
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What customers “fit”? What infrastructure “fits”?
Multi-objective optimization
(e.g. min cost, emissions, risk)
Accounts for socio-technical complexities:• Diversity• Deployment• Uncertainties • Demand-side management
ReMatch results for 10k consumers in California
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detailed graph matching for ! = 20, 21, 22 (see Figure 6) to further understand how energy is being 317utilized in our DER solutions. We find that in both cases solar provides all the energy to consumers in 318hour ! = 20 including contributing energy to charging the battery. As solar production drops off in hours 319! = 21, 22 natural gas becomes the primary source of energy with consumer type B also drawing 320significant energy from the grid and the battery. Based on these observations, we can formulate specific 321demand-side management policies and interventions that could yield reductions in the required amount of 322energy coming from natural and grid sources. For example, shifting load of consumer type B from ! = 21 323to ! = 20 using a smart thermostat pre-cooling program would yield reductions in the energy supplied by 324natural gas and the grid in addition to reducing the need for battery infrastructure in hour ! = 21. Such a 325smart and targeted demand-side management program would yield much more monetary and 326environmental emissions savings than the generic energy efficiency retrofit modeled as evident in the 327minor energy flow shifts between case I and case II. Moreover, the ReMatch framework allows us to 328validate these hypotheses rigorously. 329 330
331 332
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Solar ESolar DSolar CSolar BSolar A
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
NG ENG DNG CNG BNG A
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Grid EGrid DGrid CGrid BGrid A
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Grid EGrid DGrid CGrid BGrid A
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
NG ENG DNG CNG BNG A
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Solar ESolar DSolar CSolar BSolar AA
B
C
D
E
A
B
C
D
E
A
B
C
D
E
Sola
rN
atur
al g
asG
rid
(a) Case I (b) Case II
Hours in day (t) Hours in day (t)
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Figure 5. Temporal results of the energy utilized in each hour for (a) Case I and (b) Case II (averaged 333across all scenarios). The heat maps depict the utilization of each infrastructure type by consumer type 334
with darker color indicating higher utilization. 335 336
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Figure 6. Detailed matched graph of supply and battery infrastructure to consumers for hours ! =33920, 21, 22 (averaged across all scenarios) for (a) case I and (b) case II. The weight of the arrows in the 340
figure are representative of the amount energy being sent across that matched edge and node names are 341denoted by the name of the supply, demand or storage node and then the time interval. For example, 342
“Solar 20” is the node representing solar generation at hour ! = 20, “A 21” is the node representing 343demand for consumer type A at hour ! = 21 and “Batt 22” is the node representing battery storage at hour 344
! = 22. 345 346
(b) Case II(a) Case I
Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
B 20
B 21
B 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
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Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
B 20
B 21
B 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
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(b) Case II(a) Case I
Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
B 20
B 21
B 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
1
Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
B 20
B 21
B 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
2
Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
Batt 20
Batt 21
Batt 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
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Solar 20
NG 20
Grid 20
Solar 21
NG 21
Grid 21
Solar 22
NG 22
Grid 22
Batt 20
Batt 21
Batt 22
A 20
B 20
C 20
D 20
E 20
A 21
B 21
C 21
D 21
E 21
A 22
B 22
C 22
D 22
E 22
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50% reduction in levelized cost of electricity (LCOE)
Jain, R., Qin, J., Rajagopal, R. (under revision). “Data-driven planning of distributed energy resources (DER) amidst socio-technical complexities,” Nature Energy.
Socio-technical and data-driven modeling of slum redevelopment
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Horizontal slum morphology
Proposed vertical slum morphologies
M1
M2M3
Data + simulation to assess lighting, comfort, energy efficiency and associated health outcomes of proposed
slum rehabilitation in Dharavi, Mumbai, India
Towards a data-driven slum redevelopment toolkit
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1. On-site surveys – physical dimensions, building features
2. In-situ sensors (temp, humidity) to calibrate simulation assumptions
3. Simulate morphologies (M1, M2, M3)
Vertical redevelopment (M2, M3) could worsen thermal comfort and increase energy burdens!
Debnath, R., Bardhan, R., Jain, R. (accepted). “A data-driven and simulation approach for understanding thermal performance of slum redevelopment in Mumbai, India,” In Proceedings of the 15th IBPSA.
Urban dynamics
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Image Credit: B. Howard et al. (Modi Research Group, Columbia University)
How do we target the most “inefficient” buildings across a city?
What are the socio-technical interdependencies of urban
systems?
Performance benchmarking at the city-scale
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How do you translate data into insights and effective building energy efficiency policies?!?
Benchmarking methods + results
14Roth, J., Yang, Z., Jain, R. (under review). “Benchmarking Building Energy Efficiency at a City Scale: A Data-Driven Method Using Recursive Partitioning and Stochastic Frontier Analysis.”
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• Do energy efficient retrofits improve educational outcomes?
• How do we drive policy for dual socio-technical benefits?
Palo-Alto “living lab”
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Quantitatively and experimentally explore the coupled dynamics of urban systems through a “living lab”
Yang, Z., Gupta, K., Gupta, A., Jain, R. (accepted). “A data integration framework for urban systems analysis based on geo-relationship learning,” In Proceedings of the 2017 ASCE International Workshop on Computing in Civil Engineering, Seattle, WA.
Ontology to integrate urban data streams
16Yang, Z., Gupta, K., Gupta, A., Jain, R. (accepted). “A data integration framework for urban systems analysis based on geo-relationship learning,” In Proceedings of the 2017 ASCE International Workshop on Computing in Civil Engineering, Seattle, WA.
Enable data-driven analysis across people, building and energy systems
Conclusions / Thoughts
• Need to bring people into the loop of building and energy systems design and management for smart, sustainable cities
• New data streams could enable a deeper understanding of interactions between people and their infrastructure
• Challenge: translate data à insights– Require lots of interdisciplinary research (engineers, computer
scientists, social scientists) and civic-academic partnerships
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Check out our lab site: uil.stanford.edu
Acknowledgments Students/Post-docs: Karan Gupta, Jon Roth, Perry Simmons, Andrew Sonta, Junjie Qin, Zheng Yang (post-doc)Partners: City of SF (ENV), California Energy Commission, City of Palo AltoFunders: NSF, DOE, CIFE, Terman Faculty Fellowship, Precourt Institute, UPS Endowment
Thanks! Questions?
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