Cristin-resultat-ID: 2057902
Sist endret: 18. januar 2023, 12:48
NVI-rapporteringsår: 2022
Resultat
Vitenskapelig artikkel
2022

A systematic review of machine learning techniques related to local energy communities

Bidragsytere:
  • Alejandro Hernandez-Matheus
  • Markus Löschenbrand
  • Kjersti Berg
  • Ida Fuchs
  • Mònica Aragüés-Peñalba
  • Eduard Bullich-Massagué
  • mfl.

Tidsskrift

Renewable and Sustainable Energy Reviews
ISSN 1364-0321
e-ISSN 1879-0690
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2022
Publisert online: 2022
Trykket: 2022
Volum: 170
Artikkelnummer: 112651
Open Access

Importkilder

Scopus-ID: 2-s2.0-85139240514

Beskrivelse Beskrivelse

Tittel

A systematic review of machine learning techniques related to local energy communities

Sammendrag

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.

Bidragsytere

Alejandro Hernandez-Matheus

  • Tilknyttet:
    Forfatter
    ved Universitat Politècnica de Catalunya

Markus Löschenbrand

  • Tilknyttet:
    Forfatter
    ved Energisystemer ved SINTEF Energi AS

Kjersti Berg

  • Tilknyttet:
    Forfatter
    ved Energisystemer ved SINTEF Energi AS
  • Tilknyttet:
    Forfatter
    ved Institutt for elektrisk energi ved Norges teknisk-naturvitenskapelige universitet

Ida Fuchs

  • Tilknyttet:
    Forfatter
    ved Institutt for elektrisk energi ved Norges teknisk-naturvitenskapelige universitet

Mònica Aragüés-Peñalba

  • Tilknyttet:
    Forfatter
    ved Universitat Politècnica de Catalunya
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