开源工具Sinergym让强化学习优化建筑能耗更高效便捷。
SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning
- 基于Python构建虚拟测试平台，统一仿真与控制接口。
- 支持大规模建筑模拟、数据采集与实验可复现。
- 适合做智能楼宇控制的科研与工程人员使用。
仿真已成为建筑能耗优化（BEO）的关键工具，可在低成本下评估不同设计与控制策略。机器学习算法可利用大规模仿真数据，在无监督条件下学习最优控制，尤其在强化学习（RL）范式下表现突出。然而，缺乏开放且标准化的工具阻碍了ML与RL在BEO中的广泛应用。为此，本文提出Sinergym——一个基于Python的开源虚拟测试平台，支持大规模建筑仿真、数据采集、连续控制与实验监控。Sinergym提供一致的控制器训练与运行接口、预设基准测试、实验可视化及可复现支持，并配备完整文档。本文展示了其相较于现有框架的核心优势，说明基本用法，并通过多个典型示例验证其在基于强化学习的建筑能耗优化中的适用性。通过整合仿真、数据与控制，Sinergym支持开发智能、数据驱动的建筑运营应用，契合数字孪生技术目标。
原文摘要 · Abstract (English)
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.
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