New Methods and Data for Energy Research (NEMDER)

能源系统建模充其量是由国家或区域一级的年度数据系列驱动的。智能电表的推出沿着来自社交媒体、移动的手机和应用程序等众包平台的新形式用户数据的日益可用性，为我们提供了一个巨大的机会，可以提高我们对消费者能源行为和偏好以及英国不断变化的能源结构的了解，以接近实时的方式，以低地理分辨率。将这些数据与从其他非能源领域收集的数据相结合，并使用机器学习和分层分析方法等技术，意味着未来的能源系统研究可以在接近真实的时间内识别跳闸点，新兴模式，相互依赖性和最终用户行为。除了创建一个世界领先的，最先进的研究计划，产生这样的见解是重要的产业和政策。在前者，了解消费者的需求模式和近真实的时间的发电组合的发展，将使网络在未来的能源系统提供的间歇性可再生资源的更有效的操作。然而，这种低碳转型的轨迹是高度不确定的，因为大量的未来能源系统情景的特点。此外，将来自多个来源的数据结合起来，可以支持新服务、公司和商业模式的发展。这些新办法还有助于制定更加细致入微的政策方针，以应对消费者行为，同时利用整个能源系统在行为者多样性、社会经济、地理和网络特征、需求模式以及能源部门与运输等其他部门的相互依存关系方面的差异。否则，现有的社会经济差异和障碍点可能会扩大，导致网络出现重大瓶颈，加剧社会不平等。

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.