SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning

TL;DR

Sinergym is an open-source virtual testbed designed to facilitate reinforcement learning-based building energy optimization by providing standardized simulation, data collection, and control tools to accelerate research and application development.

Contribution

It introduces Sinergym, a comprehensive, open-source Python platform that standardizes building energy simulation and RL experimentation, addressing the lack of open tools in the field.

Findings

Sinergym enables effective RL training for building control.

The platform supports benchmarking and experiment visualization.

Demonstrated applicability in real-world building energy scenarios.

Abstract

Simulation has become a crucial tool for Building Energy Optimization (BEO) as it enables the evaluation of different design and control strategies at a low cost. Machine Learning (ML) algorithms can leverage large-scale simulations to learn optimal control from vast amounts of data without supervision, particularly under the Reinforcement Learning (RL) paradigm. Unfortunately, the lack of open and standardized tools has hindered the widespread application of ML and RL to BEO. To address this issue, this paper presents Sinergym, an open-source Python-based virtual testbed for large-scale building simulation, data collection, continuous control, and experiment monitoring. Sinergym provides a consistent interface for training and running controllers, predefined benchmarks, experiment visualization and replication support, and comprehensive documentation in a ready-to-use software library. This paper 1) highlights the main features of Sinergym in comparison to other existing frameworks, 2) describes its basic usage, and 3) demonstrates its applicability for RL-based BEO through several representative examples. By integrating simulation, data, and control, Sinergym supports the development of intelligent, data-driven applications for more efficient and responsive building operations, aligning with the objectives of digital twin technology.