Data Science-based Sizing Approach for Renewable Energy Systems

Caceres Camilo (1) , Joao Rosario (1) , Mora Jefry (1) , Didier Dumur (2)

1 UNICAMP - Universidade Estadual de Campinas = University of Campinas
2 L2S - Laboratoire des signaux et systèmes

One of the global trends in the energy sector is the inclusion of renewable sources in the grid. This need allows the reduction of greenhouse gases produced by the energetic industry, particularly with the use of fossil fuels and their effects on global warming. Therefore, these implementations need to cover the minimum requirements as balanced energy price, power demand, and a higher ROI, among others, to be reliable and compete with traditional sources. This paper presents a novel approach for sizing a hybrid renewable energy system, using data-driven decisions to meet the demand of a population in Barranquilla city. This method reduces oversizing by optimizing the number of components and increases the whole architecture's energy efficiency by managing the excess or the lack of energy through storage and backup systems.

Domaines: Automatique / Robotique; Machine Learning [stat.ML]; Energie électrique

Caceres Camilo, Joao Rosario, Mora Jefry, Didier Dumur. Data Science-based Sizing Approach for Renewable Energy Systems. 2020 IX International Congress of Mechatronics Engineering and Automation (CIIMA), Nov 2020, Cartagena de Indias, Colombia. pp.1-6, ⟨10.1109/CIIMA50553.2020.9290303⟩. ⟨hal-03105070⟩