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Smart City


Smart Energy and Smart Buildings

The Internet-of-Things (IoT) paradigm can be scaled up to the level of civil, commercial, and residential infrastructure. The prevalence of smart-grids, smart-home technologies, and Building Automation Systems make energy optimization within the reach of distributed AI approaches.


The Smart Grid is a networked system of sensors, controllers, and machines that share data to collectively optimize for a global objective. Smart Grids can intelligently manage energy usage to save costs, improve safety, and ensure human comfort. However, the availability of large amounts of data presents inherent privacy risks and computational costs.


Federated Learning can be used to keep data private while sharing intelligence to collectively increase performance. Additionally, Deep Reinforcement Learning can be used to provide adaptive controllers that tailor their operations to changing environments and individual requirements of machines in the smart grid. The STADLE platform combines these two approaches to provide private, adaptive, and distributed AI.


According to Statista[1], Smart City technologies will account for $679.5 billion of spending by public and private organizations. Whereas the largest current market for Smart Buildings is North America, the fastest-growing region is the Asia Pacific with 23% year-on-year growth [2]. Such an established market with a dynamic growth profile presents an attractive target for quantum leaps in technology provided by the STADLE platform.



Transitioning from big data to collective intelligence. Moving towards the Internet of Intelligence.

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