Self-regulation. We take it for granted in nature. But like natural ecosystems, couldn’t cities be equipped with enough sensors and feedback loops to manage most of their services automatically while making the best possible use of resources? That’s the idea behind Siemens’ City Intelligence Platform (CIP), a system now being tested in several cities around Europe, that provides the basic principles for urban decision-making with a view to the optimization of infrastructures.

To achieve this, the platform, that is currently being tested in research projects, bundles information from sources as varied as commercial and residential buildings, powerplants, and traffic and water management systems: “Cities generate huge amounts of data from a variety of sources,” explainsChristian Schwingenschlögl, project manager for the platform. Sensors and meters can measure everything that moves in a city, regardless of whether it involves bits and bytes from data transmission, kilowatt hours of electricity, liters of water, pollutant emission, or kilograms of waste. For example, cameras could forward information about vehicle speeds and traffic volumes  to a traffic management system that might, in turn, give green lights to buses that risk being late. This would enhance the appeal of public transport and reduce emissions and noise. “Our City Intelligence Platform combines these different types of data to make comprehensive analyses possible,” says Schwingenschlögl.   The result is an easy-to-follow representation of the processes that take place in a city, allowing the connections between them to be understood and making entirely new modes of operation available.

How can rush-hour traffic be better managed ? What measures can be taken to reduce power demand in office buildings? What feedback mechanisms would help residential households to lower their energy requirements? How can data transmissions be made faster? How can e-mobility be integrated into a city’s infrastructure? These are some of the areas that Schwingenschlögl and his team hope a unified city software platform comprising a range of sub-systems will be able to address and optimize.

For instance, the City Intelligence Platform has already been connected with a system known as a “Building Energy Agent” (BEA), which was developed by Siemens Corporate Technology (CT) BEA can interface, for example, with a building’s photovoltaic plant, it’s  management system, its smart meter and its electric vehicle charging system. If the building’s energy network experiences a load or generation peak, the BEA manages the photovoltaic plant inverter and the charging station to ensure that the voltage in the network stays within a prescribed range. As this happens, the agent takes factors such as the specific requirements of building operators and end users into account. CIP also interfaces with “Power Snapshots” – another development from CT -- that lets grid operators recognize critical situations at an early stage and continue operating their grids within reasonable limits. The software identifies unusual values from the wealth of data in a sizeable network area and analyzes any parts of the network that deviate from predicted values. Grid operators can now receive measurement data prepared using sophisticated graphics, with saved map materials and a representation of the grid, enabling them to respond effectively.

Not only can Siemens applications such as BEA and Power Snapshots plug into the City Intelligence Platform, but so can those from third-party providers. “There is no way we could implement every conceivable application ourselves,” says Schwingenschlögl. “We expect open interfaces to provide major added value for customers, just like in an app store.”

Thus, for example, a “Green Mobility App” has been developed as part of the European Union’s “Streetlife” project, which is intended to encourage people to use not only the fastest and cheapest transport connections but also those that have the smallest environmental footprint. The app offers an attractive graphic overview of alternative transport connections and real-time information on delays. The results from Streetlife are currently being tested in Berlin, Germany; Rovereto, Italy; and Tampere, Finland.

Another mobility application that is well suited for the City Intelligence Platform has been developed jointly between the University of Bologna in Italy and researchers from Siemens.  Here, a program simulates the interactions between electric vehicles and a city’s power distribution network. “In this system, automobiles are conceived of both as energy consumers and storage units,” explains Siemens project manager Randolf Mock. “We look, for example, at how electric cars in a company’s parking spaces could provide buffering power when the output from a building’s photovoltaic system is momentarily limited due to a few clouds.”

There are countless points of interdependence between buildings, vehicles and human needs that the software can help to simulate and visualize. In Bologna, for example, there are 20,000 electric cars that ply the city’s streets, driving, parking and recharging. “With the new software, we could simulate infrastructure effects if that number were 100,000,” says Mock. The research to do this was initiated as part of the EU’s “Internet of Energy” project and is being pursued in conjunction with 15 partners from six EU countries in the “Sensible” project.

The City Intelligence Platform, which is currently being tested in several cities, will function as an integrated urban operations headquarters. As the number of its associated apps grows, the platform is expected to become increasingly effective in terms of automating infrastructures. Project manager Schwingenschlögl’s ultimate vision is of a “comprehensive urban operating system based on highly developed algorithms that are capable of improving their performance on a lasting basis as data volumes increase.” This could amount to a self-learning, largely autonomously functioning system that could one day respond to events as they occur and thus constantly refine itself.