1 / 22

Energy Data Visualization

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

jerica
Download Presentation

Energy Data Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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…

More Related