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An IT view of Smarter Cities           Jurij Paraszczak for Smarter Cities Global Team           Director Industry Solutio...
The city – a system of systemsSystems from transportation to energy, healthcare, commerce, education,security, food, water...
Overview                                                      Asset  Smarter Cities approach creates solutions       Manag...
IBM Research: Smarter City Global engagements                                                    Dublin Traffic,         S...
Analysing Cities           Who wants what when and where                                           © 2009 IBM Corporation
Who spends what in cities ?                                  City Budgets in Aggregate                                    ...
IBM Smarter Cities Challenge     The Smarter Cities Challenge is a competitive grant program awarding $50     million wort...
Observations in working around the world with Cities   Key issues include     Ability to engage with citizens and engage t...
We are targeting the following city domains                        Building Energy   Traffic &                            ...
Underlying Science and Engineering          From paper to models                                     © 2009 IBM Corporation
Developing the Research which underlies Smarter Cities We view the Smarter City through this structure                    ...
Understanding disconnects: A warning and a simple example of acommon problem                                              ...
Using mathematics and models to drive the business activity - forexample, traffic management      Operational/ Transaction...
Advanced Analyticsis the use of data and models to provide insight to guide decisions                Analytics            ...
Managing Traffic in Stockholm                                Stockholm                                Traffic             ...
Stockholm Road Charging                          40 Gantries with 18 ingress                          points              ...
Charging to reduce traffic                             © 2008 IBM Corporation
Case Study – Stockholm Congestion Charging  Main objective – to reduce congestion by  between 10% and 15%.  Project – to b...
Analysing Traffic                    © 2008 IBM Corporation
Stream computingSupply Chain fortocritical paradigm shiftNotional Information                         represents a Decisio...
Infosphere Streams in Stockholm - why models are important                   Traffic Speed                                ...
Predicting Traffic                     © 2008 IBM Corporation
Traffic Prediction Tool (TPT) – background and motivation      The ability to capture the current traffic state and to pro...
Traffic Prediction Tool (TPT)Model: stochastic model used to predict traffic in Singapore► Issue: “real-time” is too late ...
Agent Based Analytics and prediction                                       © 2008 IBM Corporation
Large-scale Agent-based Traffic Flow SimulatorIBM Mega Traffic Simulator                                                IB...
Application of the simulator: What-If Analysis The simulator provides an experimental environment for traffic policy maker...
Water Infrastructure Management                       DC WASA                       Water                                 ...
Analytics Driven Asset Management (ADAM)                                                                  •Maintenance Pla...
ADAM: Analytics Driven Asset Management      Predictive analytics models enabling “fix before      break”      Spatial Sch...
ADAM for Water Utilities V1.0           Work                  Predictive          Usage/ Revenue        Management        ...
Examples of Advanced Reporting – Catch Basin WorkOrders                     Temporal Analysis of Work OrderCatch Basin    ...
Use casesADAM V1.0 Use cases• Manual Map Based Schedule Construction• Semi-Automated Route Completion• Multi-crew automate...
IBM Research: Smarter City Global engagements                    Dublin Traffic,                    Water, Energy  Smarter...
Smarter Cities Technology CentreDublin                                   © 2008 IBM Corporation
Transportation Developing technology to continuously assess the state of the public transport system and provide personali...
Dublin Bus – Demonstration© 2011 IBM Corporation
City Fabric Platform for gathering and analyzing Dublin city data,. Working with Dublin City on an Open Innovation Platfor...
Managing Public Safety in NYC and Chicago                      NY City + Chicago                      Public Safety       ...
Safety and Security Management  Chicago’s Virtual Shield Program    Implemented one of the most advanced city-wide intelli...
Statistical modeling, machine learning & pattern recognition are keytechnologies to enable Smart Safety and Security      ...
Selected Research & Technical Challenges Handling crowded scenes              Federated / Partitioned Architectures Finer ...
Managing Energy in Buildings                        NY Bldgs,                                    © 2008 IBM Corporation
i-BEE (IBM Building Energy and Emission) Analytics ToolSet   Saving energy, improving energy efficiency and reducing green...
Modeling Approach                    © 2008 IBM Corporation
Dashboard – Example (Energy Use & Greenhouse Summary, GISEnergy Intensity Map)  K-12 Schools                              ...
The Benefit of Analytics  Identify anomaly that can lead to failure of equipment and wasted energy, and  take corrective a...
The Role of People in Cities           Dubuque                               © 2009 IBM Corporation
IBM Research: Smarter City Global engagements             Dubuque             Water,             Energy                   ...
Green DubuqueCICERO: Citizen centric Intelligence & Resource Optimization                                                 ...
Participants Compete – IBM provides the platform   Pilot defined        Each week, individual households and teams will ha...
CICERO deployed for Resource Consumption ManagementCloud-based real-time intelligence & interaction for instrumented, inte...
Whither Weather                  © 2008 IBM Corporation
The opportunity and challenge of combining models  Weather models and resulting damage prediction for Electric  Utilities ...
IBM uses advanced weather forecasting technologies to predict powerdemand and outages - Deep Thunder our unique world clas...
13 March 2010 Nor’easter Deep Thunder Impact Forecast    Actual Outages (Repair Jobs)   Estimated Outages (Repair Jobs)   ...
Approach to Urban Flood Forecasting                            Precipitation                             EstimatesWeather ...
Integrating Systems                      © 2008 IBM Corporation
IBM Research: Smarter City Global engagements                      Rio                      Emergency                     ...
RIO Operations Center    Allows diverse agencies to share    emergency information and plan    coordinated responses      ...
Summary    IBM Research is focusing our global resources on the understanding and    management of resource usage and deri...
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Urban Systems Collaborative Seminar | Jurij Paraszczak, An it view of smarter cities

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Urban Systems Collaborative Seminar | Jurij Paraszczak, An it view of smarter cities

  1. 1. An IT view of Smarter Cities Jurij Paraszczak for Smarter Cities Global Team Director Industry Solutions and Smarter Cities IBM Research jurij@us.ibm.com With many thanks to the Research Smarter Cities team © 2009 IBM Corporation
  2. 2. The city – a system of systemsSystems from transportation to energy, healthcare, commerce, education,security, food, water, jobs and economic growth come together and interactwith each otherHow can they be managed better ?EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION + $EDUCATION • TRANSPORTATION • SOCIAL SERVICES • SAFETY • UTILITIES • HEALTHCARE • COMMUNCATION 2 15 September 2010 © 2008 IBM Corporation
  3. 3. Overview Asset Smarter Cities approach creates solutions Management Resource Optimization which simplify the way in which the myriad Pipes, Roads, Water, traffic, Wires, Bldgs, city operations act in a city and helps city etc. System of energy etc. managers make rational decisions based on Systems data and prediction Over 100 + people are working around the world are learning with our customers and deploying models and analytics which use a People Motivation & common platforms and approaches to enable Inclination repeatable processes From this work we are discovering patterns and approaches which help in this Jobs simplification, reducing cost and providing Comfort new insights Lifestyle Taking advantage of our deep scientific and City water, energy, engineering capabilities in IBM Research buildings & transport Safety & Security City Needs © 2008 IBM Corporation
  4. 4. IBM Research: Smarter City Global engagements Dublin Traffic, Stockholm Water, Energy Traffic Dubuque PNW Beijing Water, SmartGrid Bornholm Energy Shenyang Energy NY Bldgs, Energy Moscow Beijing Water, CarbonTraffic Agency Emer Security Nanotech TrafficWest Coast Tokyo PA Bldgs DC WASA Integ. City Texas Water Ranaana River Basin Water Delhi Energy Traffic Singapore Smarter City Traffic Water Rio Activity Emerg. Natural Melbourne Resources Sydney Energy & Energy LifeScience © 2008 IBM Corporation
  5. 5. Analysing Cities Who wants what when and where © 2009 IBM Corporation
  6. 6. Who spends what in cities ? City Budgets in Aggregate 50 Cities Budget : $561B Cities In Transition Mature Large IBM assessment from top 50 cities by $161B $285B (15 Cities /217 M People) (19 Cities/198M People) population 3 City types identified Mature Large Mature Medium Mature Medium $115B Cities in Transition (16 Cities/59M People) Each city type has different focus Mature Large - safety & security Mature Medium - maintenance and resource management In Transition - focus on new state of art infrastructure and resource management systems © 2008 IBM Corporation
  7. 7. IBM Smarter Cities Challenge The Smarter Cities Challenge is a competitive grant program awarding $50 million worth of technology and services over the next 3 years to 100 cities around the globe. These grants are designed to address the wide range of financial and infrastructure challenges facing cities today See http://smartercitieschallenge.org/ © 2008 IBM Corporation
  8. 8. Observations in working around the world with Cities Key issues include Ability to engage with citizens and engage their opinions and support Management of public safety Scheduling of work and activities in the face of conflicting or completely non integrated activity. Dig patch Dig Understanding of movement of people and traffic in city Caused by Lack of understanding of details of what is happening in city And use of data and analytics to determine same © 2008 IBM Corporation
  9. 9. We are targeting the following city domains Building Energy Traffic & WaterTransportation availability & purity Safety © 2008 IBM Corporation
  10. 10. Underlying Science and Engineering From paper to models © 2009 IBM Corporation
  11. 11. Developing the Research which underlies Smarter Cities We view the Smarter City through this structure Solutions Emerging area: Human interaction with Smarter City Business Data Models Optimization Decisions Infrastructure Technologies & Tools Core Technologies © 2008 IBM Corporation
  12. 12. Understanding disconnects: A warning and a simple example of acommon problem © 2008 IBM Corporation
  13. 13. Using mathematics and models to drive the business activity - forexample, traffic management Operational/ Transactional Insights System wide control Road Usage Optimization, GHG emission models Operational/ • Charge collection • More granular • Dynamic and Transactional only - disconnected charging, by location congestion based operational data pricing • Analysis of traffic • Transaction data from patterns to manage • Route planning and Development Business the management of city congestion. advice, shippers, payments concrete haulers, • Modeling traffic to limo companies, • Little automated use predict and manage theatres, taxis etc is made of real-time entire system traffic data • City-wide, dynamic traffic optimization 2008-10 2008-12? 2009-15? © 2008 IBM Corporation
  14. 14. Advanced Analyticsis the use of data and models to provide insight to guide decisions Analytics Data sources: Business automation Instrumentation Sensors Data Web 2.0 Expert knowledge “real world physics” Model: a mathematical or algorithmic representation of Models reality intended to explain or predict some aspect of it Decision executed automatically or by people Insight © 2008 IBM Corporation
  15. 15. Managing Traffic in Stockholm Stockholm Traffic © 2008 IBM Corporation
  16. 16. Stockholm Road Charging 40 Gantries with 18 ingress points Approx 320K entries/exists per day © 2008 IBM Corporation
  17. 17. Charging to reduce traffic © 2008 IBM Corporation
  18. 18. Case Study – Stockholm Congestion Charging Main objective – to reduce congestion by between 10% and 15%. Project – to build a system that would automatically tax Swedish registered vehicles entering and leaving the city centre between 6.30 and 18.30, Monday to Friday (excluding national holidays). Duration – 7 months (January - July 2006) Challenges – political sensitivity, public Results scrutiny, referendum at the end of the trial Traffic congestion in Stockholm was reduced to decide on whether to implement the by 25%, far above the original target congestion tax permanently Traffic queuing times fell by up to 50%. Journey times were faster and more predictable Stockholm bus timetables were re-written to take improvements to traffic flow into account Pollution levels in the city fell by between 10% and 15% Confidence in the system was high due to minimal enforcement and administrative errors Scheme was re-launched in August 2007 after the public referendum voted in favour of the system © 2008 IBM Corporation
  19. 19. Analysing Traffic © 2008 IBM Corporation
  20. 20. Stream computingSupply Chain fortocritical paradigm shiftNotional Information represents a Decision-making action!Transforming the Information Supply Chain reduce the time to Analytical Modeling & Information Time to Action Elapsed Time to Action Analytical Modeling & Information Operational Dashboards Planning Scorecarding Reports Bus Process & Event Mgmt Reports Ad-hoc Queries WAREHOUSE DATAMARTS DATA INTEGRATION OPERATIONAL DATA STORES SOURCES © 2008 IBM Corporation 20
  21. 21. Infosphere Streams in Stockholm - why models are important Traffic Speed Bouillet, Riabov, Verscheure Fast >140 Slow/stop Moderate Average Good © 2008 IBM Corporation Km/hr
  22. 22. Predicting Traffic © 2008 IBM Corporation
  23. 23. Traffic Prediction Tool (TPT) – background and motivation The ability to capture the current traffic state and to project it to the near future from available data sources is critical for real-time traffic management Traditional data sources Non-traditional data sources InductiveFixed loop locations, Traffic camera sparse in the network GPS device Smart phone …Infrared laser radar Passive infrared – ultrasonic sensor Historical origin-destination trip tables © 2008 IBM Corporation
  24. 24. Traffic Prediction Tool (TPT)Model: stochastic model used to predict traffic in Singapore► Issue: “real-time” is too late ► IBM Innovation: forecast the futureLittle automated use is made of the gigabytes of real-time IBM’s TPT provides a layer of intelligence by using sensortraffic data today; often, by the time it is received, it is no data in sophisticated algorithms that create relevantlonger representative of the actual traffic insights from the raw data blue = forecast black = actual red = incident 4000 results rr 3000 r rrrr r rr r r r rr volume r rr r r r r r rr TPT accurately 2000 rr r forecasts future r 1000 traffic conditions, including incidents tool screenshot 0 50 100 150 200 250 timeCurrent Focus Future Use Extension: Data Expansion Traffic Operations: Traffic Planning; Dynamic (2008 IME) develop algorithm to fill in Variable Message Sign Road Pricing; congestion gaps of real-time sensor data, resulting setting; traffic signal based tariff setting; route in a complete picture of future traffic timing, ramp metering planning & advice state, network-wide © 2008 IBM Corporation
  25. 25. Agent Based Analytics and prediction © 2008 IBM Corporation
  26. 26. Large-scale Agent-based Traffic Flow SimulatorIBM Mega Traffic Simulator IBM Mega Traffic Simulator outputbase data input Driver Behavior Model Driver CO2 emission Road Agent Map data network Traffic Origin- Vehicle census destination Link A Link B Link C Java Virtual Machine Agent Space CO2 emission for each link Agent Agent Agent Driving log Driver Model Agent Agent Agent Agent Agent Agent Agent Agent Agent 2k cars/hour Agent Agent Agent Simulation Space Agent Manager 3k cars/hour Scheduler Memory Manager Messaging Handler Message Queue Thread Manager threadthread thread thread thread thread 0.5k cars/hour Communication Manager IBM Zonal Agent-based Simulation Environment traffic volume for each link Traffic situation with more than the millions of vehicles can be simulated. Traffic situation with more than the millions of vehicles can be simulated. Traffic flow with various types of drivers behavior model can be simulated.. Traffic flow with various types of drivers behavior model can be simulated © 2008 IBM Corporation
  27. 27. Application of the simulator: What-If Analysis The simulator provides an experimental environment for traffic policy makers to perform what-if analysis concerning traffic in a large city. How the traffic would change if we introduce congestion tax. 2k cars/day If Condition1 Then … 32k cars/day 49k cars/day How the total emission would change if we introduce a new traffic policy? If Condition2 Then … Current traffic status What is the appropriate information providing service to minimize traffic congestion? If Condition3 Then … How the traffic policy and city-What is the proper traffic policy to design should be in the agingsolve traffic congestion, green issues.... society? If Condition4 Then … © 2008 IBM Corporation
  28. 28. Water Infrastructure Management DC WASA Water © 2008 IBM Corporation
  29. 29. Analytics Driven Asset Management (ADAM) •Maintenance Planning Insight, •Maintenance Scheduling Foresight and Prescriptions •Replacement Planning ADAM •Condition Assessment •Failure Cause Analysis Descriptive, Predictive and •Failure Prediction Prescriptive Analytics •Usage Analysis •Customer Analysis Data Data Operational, Failure, Usage, Condition, Customer, Location EAM / SCADA Enterprise Asset Management Scada, Sensors, Inspection, Metering Systems •Asset Management •Work Management •Service Management •Inventory / Contract •Procurement Management Assets © 2008 IBM Corporation
  30. 30. ADAM: Analytics Driven Asset Management Predictive analytics models enabling “fix before break” Spatial Schedule Optimization enables “while in the neighborhood “ scheduling Data analytics enable forecasting of water usage and detection of usage anomalies Water Pipes 1200 Miles Sewer Pipes 1800 Miles Hydrants 9000 Valves 24,000 Catch Basins 36000 Water Meters 130,000 Waster Water Capacity 370MGallons / day Water Customers 600,000 Sewer Customers 1,600,000 All from conventional historical and log data!© 2010 International Business Machines Corporation 30
  31. 31. ADAM for Water Utilities V1.0 Work Predictive Usage/ Revenue Management Maintenance Optimization Spatio-Temporal Failure Pattern and Customer Manual Scheduling Cause Analysis Segmentation Automated spatial Failure Risk based Usage Anomaly schedules PM Optimization Detection Automated Task level Failure Prediction Non-Revenue Water, rolling scheduling Energy Optimization Dynamic Mobile Replacement Usage & Revenue Work Management Planning Forecasting Advanced Reporting Predictive Analytics Optimization EAM GIS Data Water Usage Data © 2008 IBM Corporation
  32. 32. Examples of Advanced Reporting – Catch Basin WorkOrders Temporal Analysis of Work OrderCatch Basin Patterns Spatial Distribution of annual work Catch basic problem code Work classification vs Problem code visualization distribution © 2008 IBM Corporation
  33. 33. Use casesADAM V1.0 Use cases• Manual Map Based Schedule Construction• Semi-Automated Route Completion• Multi-crew automated schedulingOngoing R & DTask Level Scheduling Dynamic Re-Scheduling using GPS data © 2008 IBM Corporation
  34. 34. IBM Research: Smarter City Global engagements Dublin Traffic, Water, Energy Smarter City Activity © 2008 IBM Corporation
  35. 35. Smarter Cities Technology CentreDublin © 2008 IBM Corporation
  36. 36. Transportation Developing technology to continuously assess the state of the public transport system and provide personalized, real-time advice to riders and dynamic load-balancing opportunities to transit providers Background – GPS & other sensor technologies are transforming transportation analytics Working closely with Dublin – Demonstration visualisation of transportation network status & guidance for bus drivers Challenges – Extracting insights from real-time, noisy, irregular samples – Taking actions under uncertainty with low latency – Large volume & diversity of data© 2011 IBM Corporation
  37. 37. Dublin Bus – Demonstration© 2011 IBM Corporation
  38. 38. City Fabric Platform for gathering and analyzing Dublin city data,. Working with Dublin City on an Open Innovation Platform for Cities Background – Governments are seeking to spawn & exploit innovation & promote awareness through better Open Innovation Platform access to data of citizen’s interest Multi-City & Open Deploying significant common infrastructure for Presentation International Collaboration Collaborative Research IBM’s SC community Common – Common compute, data & network platform Data Standards & Definitions – Data repositoru – Connectivity into Dublin Systems Platform Challenges Advanced City Technology – Data & model management in City-scale environment – Tools enabling domain experts to interface with complex data & analytic challenges intuitively© 2011 IBM Corporation
  39. 39. Managing Public Safety in NYC and Chicago NY City + Chicago Public Safety © 2008 IBM Corporation
  40. 40. Safety and Security Management Chicago’s Virtual Shield Program Implemented one of the most advanced city-wide intelligent security systems The engagement is a part of Chicagos Operation Virtual Shield, a project that encompasses one of the worlds largest video security deployments In the first phase, IBM helped the City experts and network engineers design and implement a monitoring strategy infrastructure to capture, monitor and fully index video for real-time and forensic-related safety applications Korea Incheon Free Economic Zone Implemented a public safety infrastructure with intelligent video monitoring as part of the U-safety City project Built a public safety system utilizing high-resolution cameras to view and monitor activities to prevent crime and even predict possible events by recognizing and analyzing certain patterns and data in real time © 2008 IBM Corporation
  41. 41. Statistical modeling, machine learning & pattern recognition are keytechnologies to enable Smart Safety and Security Statistical Modeling is the key to handling change Background Subtraction Algorithm Blob Tracking Algorithm Object Classification Algorithm Color Classification Algorithm Machine learning enables recognition of person attributes © 2008 IBM Corporation
  42. 42. Selected Research & Technical Challenges Handling crowded scenes Federated / Partitioned Architectures Finer grained analysis of objects Analytics at the edge © 2008 IBM Corporation
  43. 43. Managing Energy in Buildings NY Bldgs, © 2008 IBM Corporation
  44. 44. i-BEE (IBM Building Energy and Emission) Analytics ToolSet Saving energy, improving energy efficiency and reducing greenhouse gas (GHG) emissions are key initiatives in many cities and municipalities and for building owners and operators. For example, New York Citys government spends over $1 billion a year on energy, and is committed to reducing the City governments energy consumption and CO2 emissions by 30% by 2030 (PlaNYC). Buildings emit about 78 percent of the city’s GHG emissions. NYC plans to invest, each year, an amount equal to 10% of its energy expenses in energy-saving measures. In order to reduce energy consumption in buildings, one needs to understand patterns of energy usage and heat transfer as well as characteristics of building structures, operations and occupant behaviors that influence energy consumption. i-BEE is physics, statistics and mathematics based building energy analytics that Assess how different energies are used (and GHG is emitted) in different ways Benchmark energy (GHG emission) uses among peer buildings Track energy consumption and its changes due the improvement actions (e.g., retrofits) Forecast future energy consumption (and GHG emission) Simulate impacts of various changes (improvements) on energy consumption and GHG emission Optimize energy consumption, efficiency and GHG emission © 2008 IBM Corporation
  45. 45. Modeling Approach © 2008 IBM Corporation
  46. 46. Dashboard – Example (Energy Use & Greenhouse Summary, GISEnergy Intensity Map) K-12 Schools © 2008 IBM Corporation
  47. 47. The Benefit of Analytics Identify anomaly that can lead to failure of equipment and wasted energy, and take corrective actions for faults Statistical Analysis (SPC, CUSUM, Time Series Model, Data Mining..) Identify underperforming buildings with respect to peer buildings and identify the root causes Multiple Regression Modeling Accurately estimate heat loss (gain) through walls, roofs, windows, and develop retrofit plans Heat Transfer Model Identify key characteristics of building structures, operations and behaviors that influence energy consumption and take actions for modifications Forecast future energy consumption and develop cost effective procurement plan of energy Forecasting Model And others… © 2008 IBM Corporation
  48. 48. The Role of People in Cities Dubuque © 2009 IBM Corporation
  49. 49. IBM Research: Smarter City Global engagements Dubuque Water, Energy © 2008 IBM Corporation
  50. 50. Green DubuqueCICERO: Citizen centric Intelligence & Resource Optimization © 2008 IBM Corporation
  51. 51. Participants Compete – IBM provides the platform Pilot defined Each week, individual households and teams will have the chance to win prizes. Each week, you will be randomly assigned to a team made up of 3-5 other Pilot members. You will not know your other team members but you can chat with them using the team chat on the site. Each week, individual households and teams will win prizes and/or will be registered to win our mid-way and final prizes! Prize drawings take place at the end of week 6 and at the end of week 1 IBM provides Cloud platform and software that aggregates and maps usage Provides metrics and competition information Tracks all usage helping development of behavioural models © 2008 IBM Corporation
  52. 52. CICERO deployed for Resource Consumption ManagementCloud-based real-time intelligence & interaction for instrumented, interconnectedcities•Deployed for water silo and work underway for electric silo•Resource optimization & decision support for maximizing city performance•Models & Incentives for changing citizen resource consumption behavior•Interest from multiple cities to join cloud delivered service © 2008 IBM Corporation
  53. 53. Whither Weather © 2008 IBM Corporation
  54. 54. The opportunity and challenge of combining models Weather models and resulting damage prediction for Electric Utilities IBM Weather Prediction System DEEP THUNDER - accurate to 2 km x 2 km area A mathematical model that describes the physics of the atmosphere – The sun adds energy, gases rise from the surface, convection causes winds Numerical weather prediction is done by solving the equations of these models on a 4-dimensional grid (latitude, longitude, altitude, time) Solution yields predictions of surface and upper air – Temperature, humidity, moisture – Wind speed and direction – Cloud cover and visibility – Precipitation type and intensity © 2008 IBM Corporation Challenge is to predict business impact of weather
  55. 55. IBM uses advanced weather forecasting technologies to predict powerdemand and outages - Deep Thunder our unique world class weatherprediction technologies Weather causes damage and outages Outages require restoration (resources) Weather Restoration takes time, people, etc. prediction Build stochastic model from weather observations, storm damage and related data Outage location, timing and response Wind, rain, lightning and duration Power Line Demographics of effected area Damage Ancillary environmental conditions prediction Work crew requirement prediction Restoration time prediction © 2008 IBM Corporation
  56. 56. 13 March 2010 Nor’easter Deep Thunder Impact Forecast Actual Outages (Repair Jobs) Estimated Outages (Repair Jobs) © 2008 IBM Corporation
  57. 57. Approach to Urban Flood Forecasting Precipitation EstimatesWeather Prediction and/or Analysis of Precipitation Rainfall Measurements Flood Prediction Refine Sensor Network Actual Flood Impacts and Model Calibration Model Calibration Impact Estimates © 2008 IBM Corporation
  58. 58. Integrating Systems © 2008 IBM Corporation
  59. 59. IBM Research: Smarter City Global engagements Rio Emergency Management © 2008 IBM Corporation
  60. 60. RIO Operations Center Allows diverse agencies to share emergency information and plan coordinated responses Part of Rios preparatory efforts for Brazils hosting of soccers World Cup in 2014 and the citys hosting of the 2016 Olympic Games. Components include Data acquisition and integration center from multiple agencies High Resolution Weather Prediction System coupled to hydrological flooding models Traffic management systems Emergency operations Integrated scheduling, optimization and allocation of processes © 2008 IBM Corporation
  61. 61. Summary IBM Research is focusing our global resources on the understanding and management of resource usage and deriving an understanding of how these resources interact The integration of technology, mathematics. IT and computer science coupled with advances in algorithms, processor speed communication bandwidth are enabling the management of cities in ways previously unimaginable World pressures from emissions, population and economic growth are driving ever increasing efficiency in the use of every resource The Smarter Cities approach enables this transition © 2008 IBM Corporation

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