Adaptive machine learning for automated modeling of residential prosumer agents

Toquica, D., Agbossou, K., Malhamé, R., Henao, N., Kelouwani, S. et Cardenas, A. (2020). Adaptive machine learning for automated modeling of residential prosumer agents. Energies, 13 (9). p. 2250. ISSN 1996-1073 DOI 10.3390/en13092250

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Résumé

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

Type de document:	Article
Mots-clés libres:	Adaptation Concept drift Data streaming Forecast Modeling Prosumer Regressor Supervised machine learning
Date de dépôt:	12 avr. 2023 12:57
Dernière modification:	12 avr. 2023 12:57
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/10633

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