Year: 2021
Type: conference-paper
Abstract: The conventional controllers for building energy management have shown significant room for improvement, and disagree with the superb developments in state-of-the-art technologies like machine learning. This paper describes an OpenAI-Gym environment for the BOPTEST framework to rigorously benchmark different reinforcement learning algorithms among themselves and against other controllers (e.g. model predictive control) by building simulation. The design philosophy of the environment and its different features are introduced. Finally, the environment is demonstrated in one emulator building model to train a reinforcement learning algorithm and compare it against a classical control logic.
Institutions: Flemish Institute for Technological Research, KU Leuven
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4285822485