Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities (Proposals Track)

Anthony Faustine (University College Dublin); Lucas Pereira (ITI, LARSyS, T├ęcnico Lisboa); Loubna Benabou (UQAR); Daniel Ngondya (The University of Dodoma)

Paper PDF Slides PDF Recorded Talk

Abstract

Community Energies (CEs) are the next-generation energy management techniques that empowers citizens to interact with the energy market as self-consumers or prosumers actively. Successful implementation of CEs will promote sustainable energy production and consumption practices; thus, contributing to affordable and clean energy (SDG7) and climate action (SDG 13). Despite the potential of CEs, managing the overall power production and demand is challenging. This is because power is generated, distributed and controlled by several producers, each of which with different, and potentially conflicting, objectives. Thus, this project will investigate the role of machine learning approaches in smartening CEs, increasing energy awareness and enabling distributed energy resources planning and management. The project implementation will be centered around proof of concept development and capacity development in Africa.

Recorded Talk (direct link)

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