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future smart cities

organising network (SON) as an example. Certain DAS networks can automatically configure and integrate new equipment and optimise the network based on data that the system itself generates. They can also self-heal by identifying faults and failures and carrying out actions to minimise the impact. Finally, they can move cell capacity from one location to another, providing on-demand capacity based on real-time feedback. In the case of automated buildings, Sinopoli suggests a few kinks will need to be ironed out before the self-learning building can be fully realised. Granular vs system-wide data. Buildings comprise many spaces, each with its own set of requirements. Granular data provides for more provision in the adequate management of each space, but requires additional equipment such as sensors, as well as tailored controls for individual space. More outlay, but eventual lower operational costs. Policies and logic. Sinopoli acknowledges that this will be onerous, but development of policy that encompasses every scenario affecting energy use, operations and ten-

8 INSIGHTS 2017

ant comfort is required to determine how a building should adapt to change and how to perform. Data is drawn from the building systems itself, as well as system-to-system communication with the utility grid and other external sources including weather or energy markets. Analytics. The same data used to develop policies will deliver an avenue for trend analysis and relationship inference, much of which already exists — energy consumption and occupancy levels, for example. This will allow building managers to predict how the building will perform in a range of scenarios. Sensors, sensors and more sensors. Today’s buildings meter and report on energy drawn from HVAC, lighting and water use, with less emphasis on plug load, where Sinopoli believes improvement can be made. He believes occupancy measurement will be the key to self-learning, but is difficult to obtain even though there are many solutions already available to capture that data, including infrared sensors and RFID tags. Many existing building subsystems already use that information to perform conditional logic — eg, if an occupant is sensed in an area and it is after

business hours, turn light levels to 10% for 30 seconds — but the self-learning building will develop this logic based on actual use, rather than predefined presumed behaviours. Proactive vs reactive operations. The final hurdle circles back to policies and requires both attitudinal and behavioural change. Rather than working on a break-fix model, the self-learning capability delivered by truly automated buildings will rely on the establishment of logic sequences, themselves based on a huge number of variables, which must be considered and used to drive policy development. In terms of pulling it all together, Sinopoli said we will need new skillsets and knowledge, as well as building analytics software that outperforms current offerings, moving beyond fault detection and diagnostics into automatic error correction and predictive maintenance — something he sees as akin to autopilot capability in aircraft. Change, it seems, is inevitable. Given the amount of behavioural adaptation and new technology required, it will be interesting to see exactly how self-learning functionality develops and and over what time frame. 

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Sustainability Matters Dec 2016/Jan 2017  

Sustainability Matters is a bi-monthly magazine showcasing the latest products, technology and sustainable solutions for industry, governmen...

Sustainability Matters Dec 2016/Jan 2017  

Sustainability Matters is a bi-monthly magazine showcasing the latest products, technology and sustainable solutions for industry, governmen...