Oxford named among most innovative cities in Europe

By: University of Oxford

Oxford has today been recognised as one the most innovative cities in Europe. 

Nuclear fusion reactor, Culham, Oxfordshire

At a ceremony hosted by the European Commission in Brussels, Oxford took its place alongside major cities including Paris and Berlin in the finals of the European Capital of Innovation awards 2016.

Oxford was the smallest city represented, competing against a shortlist comprising Amsterdam, Berlin, Eindhoven, Glasgow, Milan, Paris, Turin and Vienna, from an initial field of 36.

The University of Oxford was one of the key partners in the city’s bid to be named Europe’s iCapital – the city judged to have the best track record in supporting and promoting innovation across the community.

The title of European Capital of Innovation 2016, and a prize fund of up to €950,000, went to Amsterdam. Turin was placed second, and Paris third.

Oxford was recognised with a place on the shortlist ‘for its vision to openly share the wealth of knowledge within its world-class innovation ecosystem’.

Oxford’s bid was developed by the City Council with a team of partners from the County Council, the city’s two universities, the Low Carbon Hub, the Student Hub, the Hospital Trust, local businesses and other community organisations.

Professor Ian Walmsley, Pro-Vice-Chancellor (Research and Innovation) at the University of Oxford, said: ‘Being shortlisted among such a strong field in this year’s European Capital of Innovation awards demonstrates how Oxford’s innovation landscape punches well above its weight on a national, continental and global scale. Innovation is a key priority for the University of Oxford, from the creation of spinout companies based on our cutting-edge research to collaborations with business and industry that have a real impact on people’s lives.

‘Oxford has a complex and thriving innovation ecosystem where technologies and people converge to develop new, innovative solutions to global challenges. The University of Oxford plays an important part in this, alongside other local institutions, researchers, entrepreneurs, investors and citizens. Oxford’s shortlisting in this year’s iCapital competition will undoubtedly strengthen these partnerships across the city.’

Peter Sloman, Chief Executive of Oxford City Council, said: ‘We are delighted to have reached the finals and to have been recognised alongside such a competitive field. The fact that Oxford is in the mix with major cities like Paris and Berlin demonstrates the strength of the bid, our local economy, and our local partnerships.

‘We will continue to work to realise the benefits of innovation in and around Oxford, and aim to deliver on the spirit of the bid, which is to make Oxford a place where anyone, regardless of background, has the opportunity to bring their ideas to life. We will work with partners and the community to build on this achievement and punch above our weight as a city.’

In addition to the City Council, Oxford’s iCapital 2016 bid was funded by Oxford University, Oxford Brookes University, Oxfordshire County Council, Venturefest Oxford, Oxfordshire Local Enterprise Partnership, InOxford, and The Oxford Trust.

 

This article first appeared on the University of Oxford website on 8th April 2016

Predicting and managing energy use in a low-carbon future

By: Dyrol Lumbard, Mathematical Institute, University of Oxford

artist's impression of a low-carbon household

If effectively harnessed, increased uptake of renewable generation, and the electrification of heating and transport, will form the bedrock of a low carbon future. Unfortunately, these technologies may have undesirable consequences for the electricity networks supplying our homes and businesses. The possible plethora of low carbon technologies, like electric vehicles, heat pumps and photovoltaics, will lead to increased pressure on the local electricity networks from larger and less predictable demands.

Stephen Haben and colleagues from the University of Oxford and colleagues from the University of Reading are working with the distribution network operator (DNO) Scottish and Southern Energy Power Distribution on the £30m Thames Valley Vision project. The aim is to develop sophisticated modelling techniques to help DNOs avoid expensive network reinforcement as the UK moves toward a low carbon economy. In other words, what are some of the smart alternatives to “keeping the lights on” without simply digging up the road and laying bigger cables?

With recent advanced monitoring infrastructures (such as smart meters) we can now start using mathematical and statistical techniques to better understand, anticipate and support local electricity networks. The team has been analysing smart meter data and employing clustering methods to better understand household energy usage and discover how many different types of behaviours exist. This is turn can lead to improvements in demand modelling, designing tariffs and other energy efficiency strategies (e.g. demand side response). The researchers found different types of behaviour with varying degrees of intra-day demand, seasonal variability and volatility. Each of these therefore has different types of possible strategies in terms of reducing energy and costs. An important discovery is that energy behavioural use has very weak links with the socio-demographics, tariffs or houses size. Hence to really understand your energy demand requires the monitoring of data available through smart meters.

Forecasts can help DNOs manage and plan the networks in many ways, in particular by anticipating extremes in demand (e.g. large amounts of local generation on a sunny day). The researchers have developed a range of point and probabilistic forecasts for a wide number of relevant applications. Long term, scenario forecasts are generated using agent based models to simulate the impact of low carbon technologies. Shorter term forecasts have been developed to estimate daily demands and thus create appropriate plans for the charging and discharging cycles of batteries, helping to reduce peak overloads. These algorithms have been successfully used in silico and will soon be deployed and tested on real storage devices on the network.

Most recently the team are working on understanding limits to their models when monitoring data is unavailable or sparse. This is desirable since acquiring data and installing monitoring equipment is expensive. Can households be accurately modelled with only limited access to monitored data? If so, how much monitoring is really necessary? They have found that local energy demand is very dependent on the number and proportion of commercial and domestic properties. Such insights will be used to device workable solutions so that a DNO can choose the most appropriate (i.e. least disruptive but most cost effective) solution for different network types. Whether, for example, that is installing batteries, introducing monitoring or investing in infrastructure upgrades.

In summary, the extra visibility of household level demand through higher resolution monitoring equipment has created new opportunities for better understanding energy behavioural usage and highlighted the need for novel analytics. Demand at the individual customer level is irregular and volatile in contrast to the high voltage demands that has traditionally been investigated and thus current methods may not be applicable.  The methods necessary to reduce energy demand and promote energy efficiency sit in many areas of applied mathematics, data science and statistics. This requires mathematicians to be at the forefront of designing and creating new methods and techniques for the future energy networks.

For more information see a list of publications and the Mathematics Matters article.

 

This article first appeared on the website of the Mathematical Institute, on 8th April 2016.

A guerilla sensor network for Oxford

By: Ben Ward, TTN Oxford

We are on a mission to build a global open crowdsourced Internet of Things data network.

map of Oxfordshire

We want to promote the idea of an open sensor network, whilst understanding how to make a sustainable model.

This may be a hybrid of commercial and open, or a community fund model. For all of these we need applications to give people something to get behind.

Ben’s background is telecoms and Internet of Things. He’s founder of Flood Network, monitoring flooding around the UK using Internet of Things. He’s also the founder of Love Hz, an IoT consultancy.

He’s the main point of contact for TTN Oxford. Joe Nicholson is Director of Rufilla, an embedded IoT and M2M developer and coordinator of the Oxford Internet of Things meetup.

PLACE A GATEWAY OR JOIN THIS COMMUNITY NOW

 

This article first appeared on The Things Network on 4th April 2016