"Smart meters, data mining and machine learning: identifying energy use profiles and providing feedback that empowers energy demand reduction"

“智能电表、数据挖掘和机器学习：识别能源使用情况并提供反馈，以减少能源需求”

• 批准号: 2282006
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2282006
• 关键词: Smart; meters; data; mining; machine

"My doctoral research would involve interviews with key stakeholders in the UK smart meter rollout at a critical point in the initiative - the 2020 government deadline - while also using novel methods of data mining and machine learning to generate meaningful energy consumption profiles from large, emerging datasets. The insight from these two strands of research will then be applied to a trial energy feedback intervention. This research will be a valuable contribution to energy social science by both capturing this moment in the UK's ambitious smart meter rollout and informing future public engagement for energy demand reduction through smart grids. Research Questions: This doctoral research would be undertaken through thesis by publication, with each publication exploring one of these three interrelated questions:1. How do key stakeholders - energy suppliers, energy consumers, and various intermediaries - perceive the smart grid in terms of data access, value and intelligibility?2. Can data mining and machine learning translate large datasets of smart meter data intoenergy consumption profiles that identify inefficiency or other patterns like time shiftingand flexibility?3. What feedback and interventions based on half-hourly energy consumption profiles lead to energy demand reduction?Theoretical Framework / Methodology: A starting point for the theoretical framework and methodology of this doctoral research is the socio-technical transitions perspective developed by energy researchers (Geels et al 2018, McKenna et al 2018, Eyre et al 2018). I also have been influenced by a critique of the academic rigour of energy social science (Sovacool et al 2018). As I come from a communications and education background, my research will also be informed by diffusion theory, the elaboration likelihood model, and the theory of planned behaviour (Rogers 2010, Petty and Cacioppo 1986, Ajzen 2005). While behavioural economics provides a theoretical framework that greatly influences public policy in the UK, Hampton and Adams (2018) argue for the incorporation of practice theory into the energy policy discourse. Practice theory has also been used to explore different conceptualisations of energy users in smart grids - energy consumers vs. energy citizens (Goulden et al 2014). Fell and Shipworth (2017) propose a theory-agnostic approach to get past this divide, using a Dimension-Set Framework which characterises demand-side responses by electricity relevant dimensions and a phase state of possible options. By using the tools of data science to identify energy consumption profiles and patterns of potential efficiencies, I'm applying practice theory in the realm of big data."

“我的博士研究将涉及在英国智能电表推出的关键点-2020年政府最后期限-与主要利益相关者的访谈，同时还使用数据挖掘和机器学习的新方法，从大型新兴数据集中生成有意义的能源消耗概况。从这两个研究链的见解，然后将应用到试验能量反馈干预。这项研究将是一个宝贵的贡献，能源社会科学都捕捉这一时刻，在英国雄心勃勃的智能电表推出，并告知未来的公众参与，通过智能电网减少能源需求。研究问题：这个博士研究将通过论文出版物进行，每个出版物探索这三个相互关联的问题之一：1。关键利益相关者-能源供应商、能源消费者和各种中介机构-如何看待智能电网的数据访问、价值和可理解性？2.数据挖掘和机器学习能否将智能电表数据的大数据集转化为能源消耗概况，以识别效率低下或其他模式，如时移和灵活性？3.基于半小时能源消耗概况的哪些反馈和干预措施导致能源需求减少？理论框架/方法论：这项博士研究的理论框架和方法论的起点是能源研究人员提出的社会技术转型视角（Geels et al 2018、McKenna et al 2018、Eyre et al 2018）。我也受到了对能源社会科学学术严谨性的批评的影响（Sovacool et al 2018）。由于我来自通信和教育背景，我的研究也将受到扩散理论，阐述可能性模型和计划行为理论的影响（Rogers 2010，Petty和Cacioppo 1986，Ajzen 2005）。虽然行为经济学提供了一个理论框架，极大地影响了英国的公共政策，但汉普顿和亚当斯（2018）主张将实践理论纳入能源政策话语。实践理论也被用于探索智能电网中能源用户的不同概念-能源消费者与能源公民（Goulden et al 2014）。Fell和Shipworth（2017）提出了一种理论不可知论的方法来克服这种分歧，使用一个扩展集框架，该框架通过电力相关维度和可能选项的阶段状态来表征需求侧响应。通过使用数据科学的工具来识别能源消耗概况和潜在效率的模式，我正在将实践理论应用于大数据领域。"