Machine learning for modeling energy systems complexity

Remi Delage, Toshihiko Nakata

Research output: Contribution to conferencePaperpeer-review

Abstract

In the context of rising concerns on climate change, efforts are made to improve our energy systems with the target of a carbon-neutral society by 2050. Energy systems are complex systems yet most modeling approaches do not consider their core aspects described by complexity science so far. These aspects can be grouped into Adaptation, Nonlinearity, Self-organization, and Network, which are interrelated and without which one cannot explain energy systems complex dynamics. The present study suggests the use of machine learning models to capture energy systems complexity. Some studies have already implemented machine learning for cluster analysis or forecasting purposes, but the potential of such techniques is still far from being fully exploited. We remind some understanding of complex systems then explain how machine learning models can complement or surpass current models as well as their limitations. Concrete examples are given for real applications.

Original languageEnglish
Pages2405-2414
Number of pages10
Publication statusPublished - 2020
Event33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 - Osaka, Japan
Duration: 2020 Jun 292020 Jul 3

Conference

Conference33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020
CountryJapan
CityOsaka
Period20/6/2920/7/3

Keywords

  • Complex systems
  • Energy systems
  • Machine learning

ASJC Scopus subject areas

  • Energy(all)
  • Engineering(all)
  • Environmental Science(all)

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