1 / 29

High-Fidelity Building Energy Monitoring Network

Xiaofan Jiang and David Culler. High-Fidelity Building Energy Monitoring Network. In collaboration with Stephen Dawson-Haggerty, Prabal Dutta, Minh Van Ly, Jay Taneja. Computer Science Department University of California - Berkeley. LoCal Retreat 2009. My PG&E Statement.

tillie
Download Presentation

High-Fidelity Building Energy Monitoring Network

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. Xiaofan Jiang and David Culler High-Fidelity Building Energy Monitoring Network In collaboration with Stephen Dawson-Haggerty, Prabal Dutta, Minh Van Ly, Jay Taneja Computer Science Department University of California - Berkeley LoCal Retreat 2009

  2. My PG&E Statement • Current level of visibility • Delayed • Aggregated over time • Aggregated over space • Inaccessible • Want • Real-time • Per-appliance [Stern92], [Raaii83]

  3. Aggregate is Not Enough What caused the spike at 7:00AM? What’s the effect of turning off A? What percent is wasted by idle PCs at night? What percent is plug-load What’s the effect of server load on energy?

  4. This would be nice…

  5. Architecture • ACme application • Standard networking tools • Python driver + DB + web • ACme network • IPv6 wireless mesh • Transparent connectivity between nodes and applications • ACme node • Plug-through • Small form factor • High fidelity energy metering • Control • Simple API

  6. ACme Node

  7. Two Designs ACme-A ACme-B

  8. ACme-A vs ACme-B ACme-A ACme-B • Resistor + direct rectification + energy metering chip • Real, reactive, apparent power (power factor) • Idle power 1W • Low CPU utilization • Hall-Effect + step-down transformer + software • Apparent power • Idle power 0.1W • Medium CPU utilization A tradeoff between fidelity and efficiency

  9. ACme Node API • ASCII shell component running on UDP port provides direct access to individual ACme node: • Adjust sampling parameter • Debug network connection • Over-the-air reprogramming • Separate binary UDP port for data • Periodic report to ip_addr at frequency rate

  10. ACme Network backhaul links edge routers • IPv6 mesh routing • Each ACme is an IP router • Header compression using 6loWPAN/IPv6 (open implementation -blip) • Modded Meraki/OpenMesh as “edge router” • Diagnostics using ping6/tracert6 • ACme send per-minute digest / no in-network aggregation Acme nodes internet data repository app 1 app 2

  11. Network Performance • 49 nodes in 5 floors • Single edge router • 6 month to-date • 802.11 interference (on channel 19)

  12. ACme Application • N-tier web application • ACme is just like any data feed • Python daemon listening on UDP port and feed to MySQL database • Web application queries DB and visualize UDP Packets 6loWPAN Apache ACme Driver MySQL DB Python Daemon

  13. Visualization http://acme.cs.berkeley.edu/

  14. Building Energy Monitoring • Understanding the load tree • Disaggregation • Measurements • Estimations • Re-aggregation • Functional • Spatial • Individual

  15. Understanding the Load Tree

  16. Deployment • Edge router obtaining IPv6 address • Ad-hoc deployment • Un-planned • Online “registration” using ID and KEY • Meta data collection • Security • Online for 6 month and counting • 10 million rows

  17. Deployment

  18. Raw Data

  19. Additivity using Time Correlated Data

  20. Multi-Resolution

  21. Appliance Signature

  22. Functional Re-aggregation

  23. Correlate with Meta-data

  24. Spatial Re-aggregation

  25. Individual Re-aggregation

  26. Improvements in Energy Usage

  27. Reducing Desktop Idle Power

  28. Discussion and Conclusion Discussion Conclusion • Measurement fidelity vs coverage • Non-intrusive Load Monitoring (NILM) • IP node level API vs application layer gateway • Easy of deployment is key • DB design • Multiple input channel / power strip • ACme is a fine-grained AC metering network that provides real-time high-fidelity energy measurement and it’s easy to deploy • 3 steps to building energy monitoring – understanding load tree; disaggregation; re-aggregation

  29. Discussion • LoCal web site: http://local.cs.berkeley.edu • ACme web site: http://acme.cs.berkeley.edu • Contact: fxjiang@cs.berkeley.edu

More Related