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Energy Data Visualization

Energy Data Visualization. Bill- Jinsong Wang CS Kent Sate Fall 2013. Paper Information.

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Energy Data Visualization

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  1. Energy Data Visualization Bill-Jinsong Wang CS Kent Sate Fall 2013

  2. Paper Information • Title: Integrated energy monitoring and visualization system for Smart Green City development - Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform • Authors: Sung Ah Kim, Dongyoun Shin, Yoon Choe Thomas Seibert, Steffen P. Walz • Date: 2011

  3. Key Words • Energy monitoring • Data visualization • Smart Green City • Spatial information model • EnerISS (Energy Integrated Urban Planning & Managing Support System) • Social sensing

  4. Background • IoT, Web.o.t • Smart City(Sensor Networking/Senor Data) • Smart Grid • SCADA (supervisory control and data acquisition)/ICS

  5. Abstraction • U-Eco City is a research and development project initiated by the Korean government. • Objectives: monitoring and visualization of aggregated and real time states of various energy usages represented by location-based sensor data accrued from city to building scale. • Middleware: browser-based client • interfaces with the Google Earth and Google Maps plug-ins

  6. EnerISS Architecture • Modeler: 3D Modeling, Transfer to Solver (by E-GIS) • Viewer-Solver: Energy Demand (by E-GIS & Spatial) • Viewer-Evaluator: Analyzes Strategies (by SEE) • Viewer-EMS: does interactions (Inside Viewer or SCADA) • 5 DBs for E-GIS, Spatial, SEE, SCADA, Sensors

  7. Challenges & Characteristics • Real-World Challenge (Sensor Signal, Large Scale Data) • Functionality – Game Like (interface) • • Web based platform • • Intuitive statistical data visualization • • Real-time based sensor data collection and data aggregation • • Dynamic data loading and visualization • • Extensible city information • Energy Saving • CO2

  8. Urban data structure model • The existing GIS system and diverse Building Information Model (BIM) technologies can represent the 3D geological environment • Pre-Made

  9. Data optimization for 3D city representation • Modeler • Parametric Building • Google Earth Plugin • KML

  10. Visualization strategy • easy-to-use interface and a suitable representation method • Color, Height, 3d Geometry, Alpha Value

  11. Implementation – LODs • 4 LOD: Grid < Block < Building < Floor

  12. Implementation – Strategy

  13. Implementation – Middleware • Due to Web Base Requirement • Client  Vis Comp & DB Comp  Sensor & CIS

  14. Implementation – Data Structures • Advantages: • 1. Strong Accurate • 2. More Kinds of Data • To enhance System performance and Information Visualization Method

  15. Implementation – Testing

  16. Implementation – Addition • Large Data Treatment • Diversity Representations • Socials

  17. Conclusion & Future

  18. ANY QUESTION? Then…

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