Urban Informatics: Harnessing Data to Understand Socio-Technical Dynamics in the Urban Built Environment

Urban Informatics: Harnessing data to understand socio-technical dynamics in the urban built environment

Rishee K. Jain Asst. Professor of Civil & Env. Eng. Director, Urban Informatics Lab Stanford University [email protected] Urban Informatics Lab (UIL)

We analyze data to understand interactions between people, buildings and energy systems in cities.

Intra-building Community dynamics Urban-scale dynamics (inter-building) dynamics

2 Why people, buildings and energy in cities?

• 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

3 Opportunity: emerging data streams

City of Alexandria In-situ sensors Remote sensors Organic data

+43 M smart meters Nearly 2 B+ people NYC generates 1 TB of now in the U.S. have smart phones data every day

4 Intra-building dynamics – socio-spatial dynamics of energy usage

Lacks co-optimization of occupants + building systems

Occupants Building • Space Data-driven optimization + design systems • Time • Space • Social • Time 5 Occupancy Energy Signal Processing on Graphs (OESPg) framework

Time

Space

Occupant 1 is Social “out-of-sync”

Sonta, A., Jain, R., Gulbinas, R., Moura, J., Taylor, J. (in press). “OESPG: A Computational Framework for Multidimensional 6 Analysis of Occupant Energy Use Data in Commercial Buildings,” ASCE Journal of Computing in Civil Engineering. Community (inter-building) dynamics

Credit: microgridinsitute.org

What are community How do we plan for What are the socio- impacts of building distributed energy technical and energy energy usage? infrastructure amidst burdens of slum socio-technical redevelopment? complexities?

7 Data-driven infrastructure planning amidst socio- technical complexities

Multi-objective optimization (e.g. min cost, emissions, risk) What customers “fit”? What infrastructure “fits”? Accounts for socio-technical complexities: • Diversity • Deployment • Uncertainties • Demand-side management 8 317 detailed graph matching for ! = 20, 21, 22 (see Figure 6) to further understand how energy is being 318 utilized in our DER solutions. We find that in both cases solar provides all the energy to consumers in 319 hour ! = 20 including contributing energy to charging the battery. As solar production drops off in hours 320 ! = 21, 22 natural gas becomes the primary source of energy with consumer type B also drawing 321 significant energy from the grid and the battery. Based on these observations, we can formulate specific 322 demand-side management policies and interventions that could yield reductions in the required amount of 323 energy coming from natural and grid sources. For example, shifting load of consumer type B from ! = 21 324 to ! = 20 using a smart thermostat pre-cooling program would yield reductions in the energy supplied by 325 ReMatchnatural gas and the gridresults in addition to reducing for the 10k need for battery consumers infrastructure in hour ! = in21. SuchCalifornia a 326 smart and targeted demand-side management program would yield much more monetary and 327 environmental emissions savings than the generic energy efficiency retrofit modeled as evident in the 328 minor energy flow shifts between case I and case II. Moreover, the ReMatch framework allows us to 333 Figure 5. Temporal results of the energy utilized in each hour for (a) Case I and (b) Case II (averaged 329 validate these hypotheses rigorously. 50% reduction in levelized334 costacross all ofscenarios) electricity. The heat maps depict(LCOE) the utilization of each infrastructure type by consumer type 330 335 with darker color indicating higher utilization. (a) Case I 336 (b) Case II

A Solar A Solar 20 Solar A A 20 Solar 20 A 20 B Solar B Solar B B 20 B 20 C Solar C Solar C NG 20 Solar NG 20 D Solar D Solar D C 20 C 20 E Solar E Solar E D 20 D 20 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Grid 20

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Grid 20 A NG A 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 NG A E 20 B NG B Batt 20 NG B E 20 Batt 20 Solar 20 A 20 Solar 20 A 20 C NG C Solar 20NG C A 20 Solar 20 A 20 Solar 21 B 20 Solar 21 B 20 A 21 D NG 20 A 21NG 20 B 20 B 20 Natural gas Natural NG D NG D C 20 C 20 NG 20 NG 20 E NG E NG E C 20 C 20 B 21 D 20 B 21 D 20 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Grid 20 Grid 20 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 NG10 2111 12 13 14 15 16 17 18 19 20 21 22 23 24 DNG 20 21 D 20 A Grid 20 Grid 20 Grid A Grid A E 20 B 20 E 20 B 20 C 21 E 20 C 21 B Grid B Grid B B 20 E 20 B 20 Solar 21 Solar 21 A 21 A 21 C Grid C Solar 21Grid C Solar 21 D 21 Grid Grid 21 D 21 GridA 21 21 A 21 D B 21 B 21 Grid D Grid D B 21 NG 21 NG 21 B 21 NG 21 E 21 NG 21 E 21 E Grid E Batt 21 Grid E C 21 Batt 21C 21 C 21 C 21 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Solar10 2211 12 13 14 15 16 17 18 19 20 21 22 23 24 D 21 Grid 21 Solar 22 D 21 Grid 21 D 21 Hours in day (t) Hours in day (t) Grid 21 D 21 Grid 21 A 22 A 22 331 E 21 E 21 B 21 E 21 B 21 E 21 Jain,332 R., Qin, J., Rajagopal, R. (under revision). “Data -driven planning of distributed energy resourcesB (DER) 21 amidst socio- B 21 Solar 22 Solar 22 9 technical complexities,” Nature Energy. Solar 22 B 22 Solar 22 B 22 NG 22 A 22 NG 22 A 22 A 22 A 22

B 22 B 22 NG 22 B 22 B 22 C 22 NG 22 NG 22 C 22 NG 22

13 C 22 C 22 C 22 GridC 22 22 Grid 22 D 22 D 22 Grid 22 Grid 22 Grid 22 D 22 Grid 22 D 22 D 22 D 22

E 22 E 22 E 22 E 22 E 22 E 22 Batt 22B 22 B 22 B 22 Batt 22B 22

337 (a) Case I (a) Case I (b) Case II (b) Case II 1 1 2 2 338 1 2 339 Figure 6. Detailed matched graph of supply and battery infrastructure to consumers for hours ! = 340 20, 21, 22 (averaged across all scenarios) for (a) case I and (b) case II. The weight of the arrows in the 341 figure are representative of the amount energy being sent across that matched edge and node names are 342 denoted by the name of the supply, demand or storage node and then the time interval. For example, 343 “Solar 20” is the node representing solar generation at hour ! = 20, “A 21” is the node representing 344 demand for consumer type A at hour ! = 21 and “Batt 22” is the node representing battery storage at hour 345 ! = 22. 346

14 Socio-technical and data-driven modeling of slum redevelopment

M1 Horizontal slum morphology

M3 M2

Proposed vertical slum morphologies

Data + simulation to assess lighting, comfort, energy efficiency and associated health outcomes of proposed slum rehabilitation in Dharavi, Mumbai, India

10 Towards a data-driven slum redevelopment toolkit

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 11 performance of slum redevelopment in Mumbai, India,” In Proceedings of the 15th IBPSA. Urban dynamics

Image Credit: B. Howard et al. (Modi Research Group, Columbia University) How do we target the most What are the socio-technical “inefficient” buildings across a city? interdependencies of urban systems?

12 Performance benchmarking at the city-scale

How do you translate data into insights and effective building energy efficiency policies?!?

13 Benchmarking methods + results

• Do energy efficient retrofits improve educational outcomes? ? • How do we drive policy for dual socio-technical benefits?

Roth, J., Yang, Z., Jain, R. (under review). “Benchmarking Building Energy Efficiency at a City Scale: A Data-Driven Method 14 Using Recursive Partitioning and Stochastic Frontier Analysis.” Palo-Alto “living lab”

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, 15 Seattle, WA. Ontology to integrate urban data streams

Enable data-driven analysis across people, building and energy systems

• 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

17 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 Alto Funders: NSF, DOE, CIFE, Te r ma n Faculty Fellowship, Precourt Institute, UPS Endowment

Thanks! Questions?