Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?

We explore the challenges and possibilities of this emerging frontier

Francis G.N. Li; Chris Bataille; Steve Pye; Aidan O'Sullivan

2019

Scholarcy highlights

  • The use of quantitative models to assist decision making in energy policy started in the 1970s
  • We explore the potential for big data to contribute to the enhancement of existing energy economy modelling for policy analysis, both in the near future and over the longer-term
  • Given its potential for making the real world intersection of energy supply and demand and the economy more directly observable and measurable, the availability of big data and conditions of data abundance could force energy economy modellers to confront their basic assumptions about what their models represent and how useful they are for policy analysis
  • While the authors acknowledge the potential of big data technologies to challenge some of the long standing limitations of energy economy models, we are not blind to the potential barriers and pitfalls that face practitioners working in this area
  • While other fields of scientific research are working quickly to take advantage of the possibilities offered by big data, energy economy modelling is lagging, both in theory and in practice
  • We suggest that one specific area in the near term where this could occur is through dynamic parameterization of an existing bottom-up hybrid model with situation specific behavioural algorithms derived from big data
  • While the human imagination and the use of scenario assumptions about the future will likely always be required for energy and climate policy analysis, big data could potentially reduce much of the reliance on data extrapolation and expert judgement that exists in most models when characterising energy systems

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