Empowering cities with connected lighting · 2017. 2. 9. · On average, public lighting is more...
Transcript of Empowering cities with connected lighting · 2017. 2. 9. · On average, public lighting is more...
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Empowering cities with connected lightingLessons from Los Angeles
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street lights in LA Lighting can account forup to 40% of a city’s total energy consumption2
On average, public lighting is more than 20 years old3
2% of installed systems are connected globally, expected to reach 35% by 20253
Public lighting is everywherebut connectivity is not
~215k40%
1 Northeast Group, Global LED and Smart Street Lighting Forecast 2015-20252 European PPP Expertise Centre (EPEC), European Commission, Energy Efficient Street Lighting, 20133 Philips market analysis
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Public infrastructure should be as connected as we are
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Software level
Network level
Asset level
City asset management/dashboard
Expanding into smart city use cases that provide new value
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Open to exploringthe power of city data
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Location: TBCVision – Empowered cities with connected lighting
Enable new opportunitiesvia open, well-defined interfaces
Software level
Network level
Asset level
City asset management/dashboard
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Los Angeles is leading the wayby deploying connected lighting today
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Leveraging existing infrastructure subtly integrated in plain sight
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Approx. 1 square mile
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Lighting networkhealth monitoring
Lighting has no visible faults or issues
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Using existing energy metering functionality within connected lighting infrastructure to monitor power quality
Improved grid maintenance
Connected lightingand city monitoring
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lighting power grid health analytics by continuously measuring voltages, frequencies or environmental noise monitoring
Network health monitoringto ensure lights never go out
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• Average line voltage over the test period 118.45V (SD 28.56V) Meets ANSI C84.1 standard (110-125V)
• Overall Max. 125.8V; overall Min. 105.5V• Clearly visible daily patterns (e.g. voltage peaks between 2-3am daily)• Average line frequency 60Hz (SD 2.52Hz) Meets DOE requirements and
FNET/GridEye Consortium• Individual fixtures experienced some extremes (1% of total measurements)
Potential for advanced predictive maintenance models
Network health monitoringto ensure lights never go out
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Network health monitoringto ensure lights never go out
Average voltage fluctuation of a particular fixture throughout duration of the pilot (August 30th – November 22nd, 2016.
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Zooming in on cyclical patterns during the week of October 4th, 2016.
Network health monitoringto ensure lights never go out
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Acoustic monitoring
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Environmentalnoise variability
Acousticincident detector
Creates data-driven city planning
Urban soundscapes
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Connected lightingand city monitoring
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• Generate continuous and dense data on urban noise
• Support city-wide planning decisions like traffic planning or school placement
• Inform and monitor public health initiatives related to environmental conditions
Environmental noise monitoringprovides continuous, dense data
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Environmental noise monitoringprovides continuous, dense data
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• Average A-weighted loudness was 60 dB(a) (typical for moderate commercial area)
• Averages vary substantially by street (e.g. main streets 10 dB(a) higher than residential streets which are about 10 dB(a) lower)
• Rush hour peaks in the morning (6-7am) and afternoon (2-3pm) are visible
• Weekends are relatively consistent with weekdays however less variation
Environmental noise monitoringprovides continuous, dense data
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Environmental noise monitoringprovides continuous, dense data
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Environmental noise monitoringprovides continuous, dense data
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• Monitoring and alerts of sound levels to track compliance with regulatory limits
• Handle noise nuisancecomplaints (e.g. local councils, housing associations and environmental health officers)
• Noise maps for urban planning• Data: (A-weighted) sound
pressure (min/max/av) (advanced use cases look at more frequency bands to extract more activity information)
Urban soundscape monitoringtakes insights to the next level
Examples of urban sound maps
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Visualization and analysis (2016-09-01) by Assistant Professor Dietmar Offenhuber, PhD, Northeastern University.http://offenhuber.net
main artery
neighborhood streets
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Urban soundscape monitoringtakes insights to the next level
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Smart City Solutions
Understanding public spaces for citizens in new waysVisualization and analysis (2016-08-24) by Assistant Professor Dietmar Offenhuber, PhD, Northeastern University.http://offenhuber.net
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Visualization and analysis (2016-08-24) by Assistant Professor Dietmar Offenhuber, PhD, Northeastern University.http://offenhuber.net
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Visualization and analysis (2016-08-24) by Assistant Professor Dietmar Offenhuber, PhD, Northeastern University.http://offenhuber.net
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Collect, analyze and aggregate data in the cloud
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Share and present insights via dashboards
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Exploring new value by leveraging existing, ubiquitous infrastructure
Environmental noise monitoring
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Urban soundscape monitoring
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Lighting network health monitoring
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Lessons learned on next steps towards smart cities
Architectures: • Extensible architectures • Edge-computing benefits• Cloud architecture for flexibility
Applications: • Dense temporal and spatial diagnostic
data provides new insights• Sound data presents many opportunities
Governance: • Data policies needed (e.g. LA Open Data
Policy and Playbook) • Frameworks for pilots drive innovation
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