Energy system modelling has been driven, at best, annual data series at national or regional level. The roll-out of smart meters along with the increasing availability of new forms of user data from crowdsourced platforms such as social media, mobile phones and apps offers an immense opportunity to improve our understanding of consumer's energy behaviours and preferences and UK's changing energy mix in near real-time at a low geographical resolution. Combining this data with that collected from other non-energy domains and the use of techniques like machine learning and hierarchical analytic methods means that future energy system research can recognise tripping points, emerging patterns, interdependencies and end-user behaviours in near real time. Beyond creating a world leading, state-of-the-art research programme, generating such insights is important both for industry and policy. On the former, understanding consumer demand patterns and development of generation mix in near real time would enable a more effective operation of the network in a future energy system supplied by intermittent renewable resources. Yet, the trajectory of this low carbon transition is highly uncertain as characterised by a large number of future energy system scenarios. Moreover, combining and linking data from multiple sources can support the development of new services, firms and business models. These new approaches can also contribute to develop a more nuanced policy approach to respond to consumer behaviours whilst utilising differences across the energy system in terms of diversity of actors, socio-economic, geographic and network characteristics, demand patterns and interdependencies of energy sector with other sectors such as transport. Otherwise the risks would be widening of existing socio-economic differences and tripping points leading to major bottlenecks on the networks and exacerbating social inequalities.

Potential Impact:
This proposal aims to inform the development of energy system transition analysis tools and techniques to incorporate new methods and forms of data with a view to improve our understanding of the tripping points, emerging patterns, interdependencies and end-user behaviours in real or near real time to support innovative policy making.
Therefore it will be of benefit to several user groups:
Energy companies: the results will enable them to gain a better understanding of near real time power flows spatio-temporally. Network companies in particular, from low to medium to high voltage, will benefit in terms of grid operation, stability, control, and congestion management. Other potential industrial beneficiaries include providers of new services and technologies who will be able to use the project outcomes to more accurately define innovation strategies, allocate resources and predict the impact of choices.
Policy Community: such as the UK Government and other organisations associated with the policy process (e.g. Ofgem, Energy Saving Trust, Carbon Trust). The results of this project will enable them to develop a more nuanced policy approach to respond to consumer behaviours whilst utilising differences across the energy system in terms of diversity of actors, socio-economic, geographic and network characteristics, demand patterns and interdependencies of energy sector with other sectors such as transport.
Public Citizens: The results will benefit the general public through more efficient operation of the networks enabled by near real time visibility of energy system evolution spatio-temporally. It could also of increase awareness of personal energy consumption, how it varies across different domains (using more energy for transport but less for heating) as well as wider energy issues amongst consumers, including the opportunity to take a more active role in managing personal consumption if desired.
Academic Community: The project will bring together a diverse group of researchers including energy system modellers, policy analysts, computer and social scientists and power system engineers in an interdisciplinary setting and the results will be of interest to each of these disciplines. Therefore the project will contribute to the development of energy system transition analysis tools and techniques to incorporate new methods and forms of data with a view to improve our understanding of the tripping points, emerging patterns, interdependencies and end-user behaviours in real or near real time to support innovative policy making. It will advance research in a number of disciplines and fields (e.g. Science & Technology Studies, energy policy, scenario development and modelling). Strong links with core and UKERC Research Fund programmes as well as other RCUK energy programmes (such as TEDDI) will ensure that these contributions are fully utilized and cross-fertilized across various disciplines.
To ensure these groups benefit we plan several impact activities including workshops, reports, academic publications and other engagement activities outlined in the pathways to impact document.