  Amid growing environmental concerns and resulting energy costs, there is a
rising need for efficient Home Energy Management Systems (HEMS). Evaluating
such innovative HEMS solutions typically relies on simulations that may not
model the full complexity of a real-world scenario. On the other hand,
real-world testing, while more accurate, is labor-intensive, particularly when
dealing with diverse assets, each using a distinct communication protocol or
API. Centralizing and synchronizing the control of such a heterogeneous pool of
assets thus poses a significant challenge. In this paper, we introduce
HomeLabGym, a real-world testbed to ease such real-world evaluations of HEMS
and flexible assets control in general, by adhering to the well-known OpenAI
Gym paradigm. HomeLabGym allows researchers to prototype, deploy, and analyze
HEMS controllers within the controlled test environment of a real-world house
(the IDLab HomeLab), providing access to all its available sensors and smart
appliances. The easy-to-use Python interface eliminates concerns about
intricate communication protocols associated with sensors and appliances,
streamlining the evaluation of various control strategies. We present an
overview of HomeLabGym, and demonstrate its usefulness to researchers in a
comparison between real-world and simulated environments in controlling a
residential battery in response to real-time prices.
