Towards self-adaptive building energy control in smart grids (Proposals Track)
Juan Gómez-Romero (Universidad de Granada); Miguel Molina-Solana (Imperial College London)
Abstract
Energy consumption in buildings greatly contributes to worldwide CO2 emissions and thus any improvement in HVAC operation will greatly help tackling global climate change. We are putting forward a proposal for self-adaptive energy control in smart grids based on Deep Learning, Deep Reinforcement Learning and Multi-Agent technologies. Particularly, we introduce the concept of Deep Neural Simulation Model (DNSM) as a way of generating digital twins of buildings in which the agent can test and learn optimal operations by itself and by collaborating with other agents. Not only do we expect a reduction on energy consumption and an increment on the use of renewable sources, but also a reduction on the cost of controlling energy in buildings.