Designing a generalised reward for Building Energy Management Reinforcement Learning agents
No Thumbnail Available
Identifiers
Publication date
2021
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Citations
Export
Abstract
The reduction of the carbon footprint of buildings is a challenging task, partly due to the conflicting goals of maximising occupant comfort and minimising energy consumption. An intelligent management of Heating, Ventilation and Air Conditioning (HVAC) systems is creating a promising research line in which the creation of suitable algorithms could reduce energy consumption maintaining occupants' comfort. In this regard, Reinforcement Learning (RL) approaches are giving a good balance between data requirements and intelligent operations to control building systems. However, there is a gap concerning how to create a generalised reward signal that can train RL agents without delimiting the problem to a specific or controlled scenario. To tackle it, an analysis and discussion is presented about the necessary requirements for the creation of generalist rewards, with the objective of laying the foundations that allow the creation of generalist intelligent agents for building energy management.
Description
Keywords
Reinforcement learning, Reward, Generalised, Building, Energy efficiency, HVAC
Type
conference output
Citation
Martinez, Ruben Mulero, Benat Arregi Goikolea, Inigo Mendialdua Beitia, and Roberto Garay Martinez. “Designing a Generalised Reward for Building Energy Management Reinforcement Learning Agents.” 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) (September 8, 2021). doi:10.23919/splitech52315.2021.9566345.