University of Oulu

Xu, Y.; Ahokangas, P.; Louis, J.-N.; Pongrácz, E. Electricity Market Empowered by Artificial Intelligence: A Platform Approach. Energies 2019, 12, 4128.

Electricity market empowered by artificial intelligence : a platform approach

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Author: Xu, Yueqiang1; Ahokangas, Petri1; Louis, Jean-Nicolas2;
Organizations: 1Martti Ahtisaari Institute, University of Oulu, 90014 Oulu, Finland
2Water, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, 90014 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2019
Publish Date: 2019-11-06


Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform.

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Series: Energies
ISSN: 1996-1073
ISSN-E: 1996-1073
ISSN-L: 1996-1073
Volume: 12
Issue: 21
Article number: 4128
DOI: 10.3390/en12214128
Type of Publication: A1 Journal article – refereed
Field of Science: 512 Business and management
Funding: The authors would like to thank the P2P-SmarTest project, the VirpaD project, and the SEN2050 project consortia for their support.
EU Grant Number: (646469) P2P-SmarTest - Peer to Peer Smart Energy Distribution Networks (P2P-SmartTest)
Copyright information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (