Scientists at Siemens Corporate Technology (CT) want to make cities run as smoothly as an electric motor. With a view to cutting energy use and carbon dioxide emissions, while improving quality of life, they are piloting a scalable, high-performance data integration system called a “City Intelligence Platform.” Capable of handling inputs from systems as varied as apartment buildings, power plants, and traffic, water, and lighting infrastructures, elements of the platform are now being tested in Milan, Italy and Timisoara, Romania, where they are being used to reduce water leakage and minimize power consumption by integrating data from the cities’ water distribution and power generation infrastructures. In addition, pilot projects designed to optimize transportation are now being launched in Berlin, Germany; Rovereto, Italy; and Tampere, Finland.
Such projects are expected to generate huge amounts of data — the building blocks of new knowledge. “As data pours into a City Intelligence Platform, data analytics algorithms will be able to assess how systems throughout a city behave in real time,” explains Bernd Wachmann, head of CT’s Sustainable Cities technology and innovation project. But the long-term vision behind the Platform goes further. “What we foresee is a kind of data ecosystem,” says Christian Schwingenschlögl, the platform’s project manager. “It will be like a natural system in which everything will have a feedback loop so that the system — ultimately an entire city — regulates itself within its natural energy limits.”
A modular group of programs that can be adapted to the unique requirements of a specific city, the City Intelligence Platform gathers data from a variety of infrastructure domains, standardizes the formats, establishes relationships between their contents, and combines this content with other information, such as weather forecasts and historical data patterns. The result, according to Wachmann, is a clear, networked presentation that renders a city’s processes understandable and opens the door to identifying options for saving resources and cutting costs.