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Analytics and data lay the foundations for smart city energy consumption

Jessica Twentyman Profile picture for user jtwentyman April 9, 2014
In Austria, researchers are building a data analytics environment to explore information about energy usage generated by smart meters and sensors in an intelligent grid.

On an abandoned airfield on the north-eastern outskirts of Vienna, one of the largest urban development projects in Europe is underway. Already, the skyline at Aspern is littered with cranes.

By next year, if work goes to plan, there will be around 3,420 apartments on the 240-acre site. By 2028, Aspern will have around 8,500 apartments, along with shops, schools, health centres, offices and a subway station that will transport passengers to or from central Vienna in just 25 minutes.

But this is no run-of-the-mill construction project: it’s a “living laboratory”, according to its creators, where researchers hope to establish how renewable power sources, smart buildings and highly metered, intelligent grid technologies might best be combined to power a thriving, eco-friendly residential and commercial community.

Doctor Monika Sturm leads that research team. She’s the head of R&D at Aspern Smart City Research, a €40m joint venture formed in 2013 between the City of Vienna, utility provider Wien Energie and industrial giant Siemens.

Speaking this week in Prague at Teradata Universe, the data warehouse company’s annual European customer shindig, Sturm outlined the role that data analytics will play in the development of Aspern.

Monika Sturm

“There are so many questions to be answered,” she told attendees. “Do we use photovoltaic panels on the roofs of buildings? How can we store the energy that is generated? What kind of pumps should we use? What kinds of energy management systems will work best for us?”

But perhaps the biggest challenge lies in the fact that these investigations mustn’t disrupt the lives of residents. “We need to test our hypotheses but, at the same time, make sure that no-one will freeze, no-one will have no lights. The solutions [we develop] have to work,” said Sturm.

The only way to arrive at practical answers, she added, is to experiment with care - and then carefully analyse the data generated by those experiments. The plan is to trial a wide range of approaches, by kitting out individual buildings at Aspern with different combinations of smart-energy technologies and a hefty dose of smart meters and sensors.

The data these generate will be used as the basis for new applications that aim to influence end-user behaviour, according to Sturm, by encouraging residents to consume the power available to them in the most efficient ways possible.

She is also hoping that Aspern will provide fertile ground for new thinking in the area of managing and monitoring low-voltage (LV) grids - a new area of research, she points out, that has not yet been investigated in any real depth by researchers worldwide.

It’s no secret that smart city projects, like the one at Aspern, could provide rich pickings for analytics companies, with vendors such as Oracle, EMC, SAP, IBM and SAS Institute and Teradata all vying for a slice of a potentially huge market.

Analytics to the fore

But, as theory turns to practice, it’s likely that most of these projects will find that they need a mix of analytics technologies to explore the data generated by smart grids and smart buildings: traditional data warehouses, MPP [massively parallel processing] big data appliances, as well as frameworks like Hadoop.

The main drivers of investments in business intelligence and data analytics, according to a recent report from research company GTM Research, will be to improve asset management for grid components, bring more granularity to demand-side management, achieve better return on investment for smart meters, and speed up outage response times. The company expects culmulative spending on smart-grid-related analytics to total around $20.6 billion between 2012 and 2020, with an annual spend of $3.8 billion globally in 2020.

At Aspern, the R&D team has decided to work with Teradata, because its Unified Data Architecture (UDA) strategy will enable them to closely integrate the different analysis platforms needed.

But understanding the correlations between energy sources, building use and the impact of weather conditions, for example, will still pose huge interpretation challenges, according to Sturm. Her team will need to develop a wide range of specialised algorithms for querying and reporting on data in preparation for the time when the first buildings at Aspern are occupied and their energy management systems start running.

But she’s hopeful their findings will influence other smart-city and smart-grid projects worldwide. “By analysing the most efficient mixes of technologies and their influence on end-user behaviour, we expect that data analytics will lead to new paths for energy optimisation in smart cities everywhere, for the benefit of all.”

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