Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents

David Toquica, Kodjo Agbossou, Roland Malhamé, Nilson Henao, Sousso Kelouwani and Alben Cardenas
Additional contact information
David Toquica: Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada
Kodjo Agbossou: Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada
Roland Malhamé: Department of Electrical Engineering, Polytechnique Montréal, C.P. 6079, Succ. Centre-Ville, Montréal, QC H3C 3A7, Canada
Nilson Henao: Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada
Sousso Kelouwani: Department of Mechanical Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada
Alben Cardenas: Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC G8Z 4M3, Canada

Energies, 2020, vol. 13, issue 9, 1-19

Abstract: An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.

Keywords: adaptation; concept drift; data streaming; forecast; modeling; prosumer; regressor; supervised machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/9/2250/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/9/2250/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:9:p:2250-:d:353812

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().