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Multi-model ecologies for shaping future energy systems: Design patterns and development paths

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  • Bollinger, L.A.
  • Davis, C.B.
  • Evins, R.
  • Chappin, E.J.L.
  • Nikolic, I.

Abstract

As energy systems grow more complex, modeling efforts spanning multiple scales, disciplines and perspectives are essential. Improved methods are needed to guide the development of not just individual models, but also multi-model ecologies – systems of interacting models. Currently there is a lack of knowledge concerning how multi-model ecologies can and should be designed to facilitate adequate understanding of energy system complexity and its consequences. Via an analysis of twelve multi-model initiatives both within and outside the energy domain, this paper elucidates possible design patterns and development paths for multi-model ecologies.

Suggested Citation

  • Bollinger, L.A. & Davis, C.B. & Evins, R. & Chappin, E.J.L. & Nikolic, I., 2018. "Multi-model ecologies for shaping future energy systems: Design patterns and development paths," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3441-3451.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:3441-3451
    DOI: 10.1016/j.rser.2017.10.047
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    References listed on IDEAS

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    Cited by:

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    2. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    3. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
    4. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Manfren, Massimiliano & James, Patrick AB. & Tronchin, Lamberto, 2022. "Data-driven building energy modelling – An analysis of the potential for generalisation through interpretable machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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