Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?
Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?
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DOI：
10.1016/j.apenergy.2019.02.002
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发表时间：
2019-04
期刊：
影响因子：
11.2
通讯作者：
F. Li;C. Bataille;S. Pye;A. O'Sullivan
中科院分区：
文献类型：
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作者：
F. Li;C. Bataille;S. Pye;A. O'Sullivan
能源经济模型是21世纪能源和气候问题决策的核心，例如为《巴黎协定》下深度脱碳战略的设计提供信息。制定旨在实现能源系统这种根本性转变的政策，将需要对最终用户需求、效率和燃料转换进行更深入的建模，以及越来越需要对区域、部门和代理人进行分类，以了解技术、司法和政策细节。构建和使用这些模型需要在细节级别、系统边界的大小和可用的计算资源之间进行复杂的权衡。描述能源系统关键部门和相互作用的数据的可用性也是模型结构和参数化的关键驱动因素，数据稀缺导致了许多盲点和设计妥协。然而，我们可能很快就会生活在一个数据丰富的世界里，这可能会使能源经济模型中以前不可能达到的分辨率和覆盖水平成为可能。但是，尽管大数据概念和平台已经开始在一些选定的能源研究应用中使用，但它们改善甚至彻底革新能源经济模型的潜力在现有文献中几乎完全被忽视了。在这篇文章中，我们探讨了这一新兴前沿的挑战和可能性。我们确定了该领域的关键差距和机会，并参考了关于不确定性下的决策、情景分析和科学哲学的现有文献，为指导未来将大数据应用于能源经济建模制定了基本概念。
Energy economy models are central to decision making on energy and climate issues in the 21st century, such as informing the design of deep decarbonisation strategies under the Paris Agreement. Designing policies that are aimed at achieving such radical transitions in the energy system will require ever more in-depth modelling of end-use demand, efficiency and fuel switching, as well as an increasing need for regional, sectoral, and agent disaggregation to capture technological, jurisdictional and policy detail. Building and using these models entails complex trade-offs between the level of detail, the size of the system boundary, and the available computing resources. The availability of data to characterise key energy system sectors and interactions is also a key driver of model structure and parameterisation, and there are many blind spots and design compromises that are caused bydata scarcity. We may soon, however, live in a world ofdata abundance, potentially enabling previously impossible levels ofresolutionandcoveragein energy economy models. But while big data concepts and platforms have already begun to be used in a number of selected energy research applications, their potential to improve or even completely revolutionise energy economy modelling has been almost completely overlooked in the existing literature. In this paper, we explore the challenges and possibilities of this emerging frontier. We identify critical gaps and opportunities for the field, as well as developing foundational concepts for guiding the future application of big data to energy economy modelling, with reference to the existing literature on decision making under uncertainty, scenario analysis and the philosophy of science.