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Tuesday , June 18 th , 2014

Case Studies in Lighting Efficiency James L. Welch Hall Intelligent Lighting Control Kenneth Seeton and Bruce Pelton. Tuesday , June 18 th , 2014. Lighting is the Largest Controllable Load. http:// www.eiu.edu /sustainability/ you_energy.php. Enlighted at a Glance.

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Tuesday , June 18 th , 2014

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  1. Case Studies in Lighting EfficiencyJames L. Welch Hall Intelligent Lighting Control Kenneth Seeton and Bruce Pelton Tuesday, June 18th, 2014

  2. Lighting is the Largest Controllable Load http://www.eiu.edu/sustainability/you_energy.php

  3. Enlighted at a Glance Best-in-Class: Cost effective solution that installs quickly and painlessly Market position: Over 35 Fortune 500 clients. 30+ million ft2 (2 788 000 m2) since 2011. Rapid Payback: Avg. payback 2-3 years with 50%-75% lighting energy savings New Intelligence: Data and analytics for HVAC control, ADR, enterprise-wide efficiencies

  4. Traditional Zonal Lighting and Controls Wiring • 3 separate systems for power, control and measurement • Significant design time and field set up • Limited control – zone-per-room • Limited Intelligence • Not easily expanded, upgraded or re-provisioned

  5. Enlighted Building-Level Architecture (wireless) ROOM CONTROLLER • Push control profiles on command • Pull sensor data every 5 minutes • No mesh ─ point to point • IEEE 802.15.4 standard • 2.4 GHz ISM spectrum • AES-128 bit encryption 5

  6. Zonal vs Granular

  7. Title 24 / ASRAE 90.1 Compliance • Occupancy Sensors • Auto Shut-Off/Dimming • Outdoor Scheduling, Motion and Photocontrol • Demand Response • Daylight Harvesting to Secondary and Skylit Zones • Submetering of Loads Instead of Disaggregation • Smallest Zone for Best Power Adjustment Factors • Stairwell Light Dimming • Auto Shut-Off and Manual Controls in Each Space • Emergency/Egress Lighting Shutoff • Plug Load Control

  8. Zonal Responsive Lighting Solutions Energy saving lighting control systems for open-plan offices: a field study General Services Administration, Joy Wei, Abby Enscoe, & Francis Rubinstein Galasiu, A. D., Newsham, G. R., Suvagau, C., & Sander, D. M. (2007). Lawrence Berkeley National Laboratory Principal Investigator: Francis Rubinstein .

  9. Sensor Local Microprocessor Eliminates Network Dependency Hardwired to Fixture Dependability Transceiver Capability Range Extension Dry, Damp and IP65 Multiple Optics High Bay, Pole, Office, Garage • Occupancy • Dual Tech • DPIR – Motion • Light/Logic • Light Levels • Daylight • Source/Fixture Degradation Offset • Thermal • Thermal Distribution • Airflow Patterns • Inform BMS & HVAC via BACnet • Push control profiles on command • Pull sensor data every 5 minutes • No mesh ─ point to point • IEEE 802.15.4 standard • 2.4 GHz ISM spectrum • AES-128 bit encryption

  10. Network Components Control Unit Gateway 250+ Sensors 300ft(100m)Range POE Powered Normally Closed Relay • Lights on with any problem Power Metering 0-10V Control Line • LED, Fluorescent, HID, Plasma, OLED Wireless Switch Dim Brighten Scenes Automatic Energy Manager 1000+ Sensors Continuous Local Backup Backup to network share Future Cloud Connections

  11. Real World Examples

  12. %On Watts Foot Candles 19 3 0 21 8 0.27 26 11 0.36 31 13 0.45 41 18 0.64 51 22 0.8 61 28 0.96 71 33 1.12 75 35 81 38 1.26 91 43 1.4 100 44 1.46 original 100w HPS new 44w savings 64% enlighted savings based on 44w 74% Profile set at 25% min 75% max with 1 min delay Sensity

  13. Computer Lab, Geo Lab, Classroom, Corridor

  14. Actually Savings 213-12-0500290287

  15. Realtime Status of Each Fixture

  16. Profile Management

  17. Hallway Profile

  18. Reporting

  19. Energy Usage and Savings by Measure Reduction Through Task Tuning Daylight Harvesting Occupancy Reduction Net Consumption

  20. Carbon Reduction

  21. Energy Consumption Report

  22. Consumption by Space Type

  23. Fixture Outage Reporting

  24. Time of Use Pricing Model for ADR

  25. Commissioning X

  26. Fixtures Discovered and Tested Pending Strobing and Placement

  27. Occupancy and Utilization

  28. Motion & Occupancy Data Analysis

  29. Space Utilization: Occupancy Maps Occupancy heat map by area highlights specific areas that are underutilized and areas that are heavily utilized.

  30. Optimizing HVAC with New Data Pervasive temperature sensors describe hot and cold zones in the building Pervasive occupancy sensors indicate set-back zones for active duty cycling

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