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

我的博士研究将包括在倡议的关键时刻-2020年政府截止日期-采访英国智能电表推出的关键利益相关者，同时还使用数据挖掘和机器学习的新方法，从大型新兴数据集生成有意义的能源消耗概况。来自这两个研究线索的洞察力随后将被应用于一项试验性的能量反馈干预。这项研究将是对能源社会科学的宝贵贡献，既可以捕捉到英国雄心勃勃的智能电表推出的这一时刻，也可以为未来通过智能电网减少能源需求的公众参与提供信息。研究问题：这项博士研究将通过逐篇论文的方式进行，每个出版物都探索以下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--如何在数据访问方面感知智能电网，价值和可理解性？2.数据挖掘和机器学习能否将大量智能电表数据转化为能源消耗概况，以识别低效或其他模式，如时移和灵活性？3.基于半小时能源消耗概况的反馈和干预措施会导致能源需求减少？理论框架/方法论：这项博士研究的理论框架和方法的起点是能源研究人员发展的社会-技术转型视角(Geels等人2018年，McKenna等人2018年，Eyre等人2018年)。我还受到了对能源社会科学学术严谨性的批评(Sovoool等人，2018年)。由于我来自传播和教育背景，我的研究也将受到扩散理论、精化可能性模型和计划行为理论的影响(Rogers 2010，Petty和Ccioppo 1986，Ajzen 2005)。虽然行为经济学提供了一个极大地影响英国公共政策的理论框架，但汉普顿和亚当斯(2018)主张将实践理论纳入能源政策话语。实践理论也被用来探索智能电网中能源用户的不同概念--能源消费者和能源公民(Goulden等人，2014)。Fell和Shipworth(2017)提出了一种理论不可知的方法来克服这种分歧，使用了一个维度集框架，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。

"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."