The potential of deep learning to reduce complexity in energy system modeling-Reference-Cited by-同舟云学术

The potential of deep learning to reduce complexity in energy system modeling

Published:2021-11-22 Issue: Volume: Page:
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Intl J of Energy Research

Author:

Köhnen Clara Sophie¹,Priesmann Jan¹^ORCID,Nolting Lars¹^ORCID,Kotzur Leander²³^ORCID,Robinius Martin⁴^ORCID,Praktiknjo Aaron¹⁵^ORCID

Affiliation:

1. Chair for Energy System Economics, Institute for Future Energy Consumer, Needs and Behavior (FCN), E.ON Energy Research Center RWTH Aachen University Aachen Germany

2. Techno‐economic Systems Analysis (IEK‐3), Forschungszentrum Jülich Institute of Energy and Climate Research Jülich Germany

3. JARA‐ENERGY Jülich Aachen Research Alliance Jülich Germany

4. Umlaut Energy GmbH Aachen Germany

5. JARA‐ENERGY Jülich Aachen Research Alliance Aachen Germany

Funder

Bundesministerium für Wirtschaft und Energie

Publisher

Wiley

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.7448

Reference59 articles.

1. Herausforderungen der künftigen Energieversorgung

2. Introduction to Energy Systems Modelling

3. Review of models and actors in energy mix optimization – can leader visions and decisions align with optimum model strategies for our future energy systems?

4. Compromises in energy policy—Using fuzzy optimization in an energy systems model

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