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New Methods and Data for Energy Research (NEMDER)

能源研究新方法和数据 (NEMDER)

基本信息

批准号：
EP/R002320/1
负责人：
Nazmiye Ozkan
金额：
$ 7.65万
依托单位：
CRANFIELD UNIVERSITY
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR002320%2F1
关键词：
New Methods Data Energy Research

项目摘要

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.

能源系统建模充其量是由国家或地区层面的年度数据系列驱动的。智能电表的推出以及来自社交媒体、手机和应用程序等众包平台的新型用户数据的可用性不断增加，为我们以低地理分辨率近乎实时地了解消费者的能源行为和偏好以及英国不断变化的能源结构提供了巨大的机会。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近乎实时地识别触发点、新兴模式、相互依赖性和最终用户行为。除了创建世界领先、最先进的研究计划之外，产生此类见解对于行业和政策都很重要。就前者而言，近乎实时地了解消费者需求模式和发电组合的发展将使网络在由间歇性可再生资源提供的未来能源系统中更有效地运行。然而，由于未来能源系统的大量情景，这种低碳转型的轨迹具有高度不确定性。此外，组合和链接多个来源的数据可以支持新服务、公司和商业模式的开发。这些新方法还有助于制定更细致的政策方法，以应对消费者行为，同时利用整个能源系统在参与者多样性、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赖性方面的差异。否则，风险将是现有社会经济差异和触发点扩大，导致网络出现重大瓶颈并加剧社会不平等。