SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning
This addresses the problem of hindered widespread application of ML/RL in building energy optimization for researchers and practitioners, though it is incremental as it builds on existing simulation and control frameworks.
The paper tackles the lack of open and standardized tools for applying machine learning and reinforcement learning to building energy optimization by introducing Sinergym, an open-source virtual testbed that provides a consistent interface, benchmarks, and support for simulation and control, enabling more efficient building operations.
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.