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Communication Dans Un Congrès Année : 2020

Machine learning application to priority scheduling in smart microgrids

Résumé

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.
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Dates et versions

hal-03135745 , version 1 (09-02-2021)

Identifiants

Citer

Aicha Dridi, Hassine Moungla, Hossam Afifi, Jordi Badosa, Florence Ossart, et al.. Machine learning application to priority scheduling in smart microgrids. IWCMC 2020: 16th International Wireless Communications and Mobile Computing, Jun 2020, Limassol (online), Cyprus. pp.1695-1700, ⟨10.1109/IWCMC48107.2020.9148096⟩. ⟨hal-03135745⟩
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