Building energy efficiency recommendations with reinforcement learning

Abstract

With buildings accounting for a significant portion of the grid’s total energy demand, it is evident that consumers should be engaged in energy efficiency. In this work, we propose a reinforcement learning approach that conducts energy efficiency recommendations for buildings, in the form of load-shift suggestions for different devices/assets. The adopted methodology can continuously learn consumer energy behavior and preferences to minimize energy costs, while preserving comfort by jointly training a single agent for all the building assets. The agent utilizes user feedback on the recommendations and integrates it in the reward function. Preliminary experiment results with simulated data show that the agent’s reward is increasing throughout time.

Odyssefs Diamantopoulos Pantaleon

PhD Student

I am academically interested in creating smart AI agents that are able to perform and adapt in dynamic environments. I am mainly utilizing Reinforcement Learning, Game Theory and Multimodality to achieve my goals.