Towards self-adaptive building energy control in smart grids

Image credit: FUNIBER

Abstract

Buildings are the largest energy-demanding sector in the world, representing over one third of the total worldwide consumption and a similarly important source of CO2 emissions. We envision a future energy system in which building control will be performed by autonomous self-adaptive agents that, with minimal configuration, will learn how to operate the HVAC equipment more efficiently and how to collaborate with other actors of the grid.

Publication
In NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning
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Juan Gómez Romero
Associate Professor of Artificial Intelligence

My research interests include Machine Learning, Information Fusion and Knowledge Representation.