TCS launches smart software to cut LED power consumption

LED Streetlight

Tata Consultancy Services (TCS) on Monday unveiled an intelligent software that allows cities to derive greater value from the costly LED lighting by reducing the payback period to almost half.

This would allow cities to invest in other smart city projects, cut energy consumption using self-learning algorithms and improve public safety by responding to real-time changes in traffic, weather and people movement.

“We are just scratching the surface of what is possible when cities intelligently connect urban data sources. Just as we are witnessing in retail, banking and other customer-centric markets, cities will soon deliver a superior experience for digitally savvy citizens and visitors,” Seeta Hariharan, General Manager and Group Head, TCS Digital Software & Solutions Group, said in a statement.

‘The Intelligent Urban Exchange’ (IUX) for ‘Adaptive Streetlight Optimisation’ also helps cities to jump start smart city projects in other domains by leveraging smart streetlight wide area networks (WAN) and a common data analytics platform.

Research firm Northeast Group LLC predicted that over the next 10 years, 280.2 million LED streetlights would be added across 125 countries, reaching a penetration rate of 89 per cent by 2026.

The firm estimated that 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.

According to IT industry analyst firm Gartner and its “Smart Cities Look to the Future March 2017” report, by 2020, ten per cent of smart cities would use streetlamps as the backbone for their smart city WAN.

The cloud-based IUX software capitalises on the efforts by cities of all sizes to replace power-hungry conventional streetlights that consume 40-50 per cent of a city’s energy budget.

IUX can deliver an additional 15-25 per cent in savings — on top of the 50 per cent energy savings from LED lighting alone — by optimising streetlight operation using machine learning and predictive analytics on real-time and historic data.