Data for Digital Decarbonisation (3D): A FAIR approach to energy demand data in buildings

一个突出的事实是，与一个问题有关的数据越清晰和结构越好，这个问题就越容易解决。用Linus Torvalds的话来说，他是Linux操作系统的发明者，今天的大多数互联网都运行在这个操作系统上：“糟糕的程序员担心代码。优秀的程序员关心数据结构和它们之间的关系。“- Linus Torvalds该提案是关于建筑物能源需求数据如何构建和存储的基础性早期研究。结果将是新数据结构和技术的概念验证，以大大提高我们为零碳建筑做出设计和政策决策的能力。拟议的工作选择了现有的、已建立的开放获取能源数据集，并使用FAIR开放数据指南（https：//www.go-fair.org/fair-principles/）将其转换为高度结构化的版本。这是将能源数据社区从“封闭世界”转移到“开放世界”数据模型以增强开放性，透明度和协作的一个小而关键的第一步。 为什么要这样做呢？减少建筑物的能源需求对于实现零碳经济具有关键的国家重要性。这在很大程度上是一项政策挑战，因为许多经过验证的建筑改造技术已经存在，历届政府都发现很难为该部门制定长期成功的政策举措。或许可以从政府最近应对新冠肺炎危机的政策中找到灵感。在这方面，来自许多合作的流行病学学术团体的数据和模型的结合增加了采取行动的论据的份量，并有助于推动流行病应对政策。在应对气候变化的能源需求方面也需要采取类似的举措;而不是孤立的学术和/或专业团体开发只有他们才能理解的数据和工具，而是需要从根本上改变这一领域内的合作和可重复性，以便为政策设计和实施创造重要的动力。流行病学和一般医学领域一直处于开发新数据标准和结构的最前沿，因为避免数据解释中的误解至关重要。这极大地启发了FAIR开放数据指南的发展，该指南已被开放研究运动广泛采用。FAIR代表可查找、可扩展、可互操作和可重用，该指南为开放研究数据提供提供了一套广泛的原则。虽然被广泛讨论，但能源需求领域的数据集很少完全符合FAIR指南。最后，要满足FAIR准则的要求，就需要从简单的数据结构（如Excel电子表格和CSV文件）转变为更精细、机器可读和标准化的数据表示形式（如节点边缘图和资源描述框架（RDF）结构）。这些更复杂的数据结构最初很难创建，但在信息和信息点之间的关系中包含更多的嵌入式含义和逻辑，因此它们自然更适合复杂的分析和其他用户的理解和重用。这项工作的前提是，全行业转向符合FAIR的能源需求数据是一个基本的，必要的要求，为能源研究提供真正的合作和创新环境，为未来的技术，设计和政策发展提供信息。

It is a salient truth that the better clarity and structure of data relating to a problem, the easier that problem will be to solve. In the words of Linus Torvalds, the inventor of the Linux operating system upon which the majority of today's internet runs: "Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds This proposal is fundamental, early stage research into how data for energy demand in buildings is structured and stored. The output will be proof-of-concept of new data structures and techniques to greatly improve our ability to make design and policy decisions for zero-carbon buildings. The proposed work takes a selection of existing, established open-access energy datasets and converts them to highly-structured versions using the FAIR open data guidelines (https://www.go-fair.org/fair-principles/). This is a small but crucial first step in moving the energy data community from 'closed-world' to 'open-world' data models to enhance openness, transparency and collaboration. Why do this? Reducing the energy demand from buildings is of key national importance in delivering the zero carbon economy. This is largely a policy challenge, as many proven building retrofit technologies already exist and successive governments have found it very difficult to develop long-lasting successful policy initiatives for the sector. Perhaps inspiration can be found in the Government's recent policy response to the Covid-19 crisis. Here the combination of data and modelling from many cooperating epidemiology academic groups added weight to the arguments for action and helped drive the policy of the pandemic response. A similar initiative is needed in the energy demand response to climate change; rather than isolated academic and/or professional groups developing data and tools which only they understand, a fundamental shift in required to transform collaboration and reproducibility within the field so that significant momentum for policy design and implementation can be created. Epidemiology, and the medical field in general, have been at the forefront of developing new data standards and structures due to the critical importance to avoid misunderstandings in data interpretation. This greatly informed the development of the FAIR open data guidelines which have been widely adopted by the Open Research movement. FAIR stands for Findable, Accessible, Interoperable and Reusable, and the guidelines give a set of broad principles for open research data provision. Although widely discussed, very few datasets in the energy demand field are fully compliant with the FAIR guidelines. Of particular note is the requirement to use Unique Identifiers to represent concepts and information.Ultimately, meeting the FAIR guidelines require a shift away from simple data structures such as Excel spreadsheets and csv files to more refined, machine-readable and standardised data representations such as node-edge graphs and Resource Description Framework (RDF) structures. These more complex data structures are harder to initially create but contain much more embedded meaning and logic, both in the information and the relationships between the points of information, so that they naturally are better suited for complex analyses and for other users to understand and reuse. The premise of this work is that a sector-wide move to FAIR-compliant energy demand data is an underlying, necessary requirement for a truly collaborative and innovative environment for energy research informing future technology, design and policy developments.