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Machine Learning Smart City Software from TCS Slashes Payback Period for LED Streetlights

Tata Consultancy Services has unveiled intelligent software that allows cities to derive greater value from costly LED lighting by reducing the typical four to five year payback period almost in half – to just two to three years. This allows cities to invest in other smart city projects sooner, cuts energy consumption using self-learning algorithms and improves public safety by responding to real-time changes in traffic, weather and people movement.

Available from TCS Digital Software & Solutions Group, Intelligent Urban Exchange (IUX) for Adaptive Streetlight Optimization also helps cities to jump start smart city projects in other domains, such as water and transportation, by leveraging smart streetlight wide area networks and a common data analytics platform.

“The global switch to smart street lighting is an open invitation for every city to begin their smart city journey,” said Seeta Hariharan, General Manager and Group Head, TCS Digital Software & Solutions Group. “Like the dawn of the Internet, we’re just scratching the surface of what’s possible when cities intelligently connect scores of new urban data sources. Just as we’re seeing in retail, banking and other customer-centric markets, cities will compete on their ability to deliver a superior experience for digitally savvy citizens and visitors.”

The cloud-based IUX software capitalizes on ambitious efforts by cities of all sizes to replace power-hungry conventional streetlights that consume 40% to 50% of a typical city’s energy budget. Designed for both LED and conventional streetlights, it acts like a virtual energy advisor for mayors, city managers and urban planners.

IUX can deliver an additional 15 to 25 percent in savings – on top of the 50 percent energy savings from LED lighting alone – by optimizing streetlight operation using machine learning and predictive analytics on real-time and historic data. It enables individual streetlights to respond to real-time events by automatically adjusting city lighting to suit changes in crime patterns, traffic, people movement, and weather.

For example, streetlight luminosity can be automatically increased to enhance public safety when crowds amass around a traffic accident waiting for first responders. The software can also recommend streetlights be dimmed to save money when bad weather keeps people indoors, increased in response to pedestrian activity or adjusted to resolve light pollution complaints.

Cash strapped cities around the world are installing energy efficient LED lighting to cut costs, meet aggressive government sustainability mandates and free up funds for smart city projects. Research firm Northeast Group LLC predicts that over the next 10 years, 280.2 million LED streetlights will be added across 125 countries, reaching a penetration rate of 89 percent by 2026. The firm estimates public LED street lighting represents a $69.5 billion market opportunity over the next decade, with $12.6 billion invested in “smart” networked streetlights from 2016 to 2026. Furthermore, according to IT industry analyst firm Gartner and its Smart Cities Look to the Future March 2017 report, by 2020, 10 percent of smart cities will use streetlamps as the backbone for their smart city WAN (wide area networks).

IUX is designed as a springboard for launching other smart city applications, because it doesn’t require cities to purchase another smart cities platform for other domains such as water or transportation. Its Connected Intelligence Platform enables data from energy, water, transportation, and other domains to be accessed, exchanged and analyzed. This makes IUX useful for crisis management and emergency response, which depend on data across multiple city systems.

A city running IUX for Adaptive Streetlight Optimization can easily add other smart city applications such as Intelligent Water Management or Intelligent Transportation. A city using IUX for Intelligent Energy, Water Management and Transportation in unison could potentially predict a water main break, automatically reroute buses to avoid affected streets and brighten specific streetlights to assist repair crews — automatically.

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