Sinergym – A virtual testbed for building energy optimization with Reinforcement Learning

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Alejandro Campoy-Nieves, Antonio Manjavacas, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero
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引用次数: 0

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.
Sinergym - 利用强化学习优化建筑能耗的虚拟试验平台
模拟已成为建筑能源优化(BEO)的重要工具,因为它能以低成本评估不同的设计和控制策略。机器学习(ML)算法可以利用大规模仿真,在没有监督的情况下从海量数据中学习最优控制,特别是在强化学习(RL)范例下。遗憾的是,由于缺乏开放和标准化的工具,阻碍了 ML 和 RL 在 BEO 中的广泛应用。为了解决这个问题,本文介绍了 Sinergym,一个基于 Python 的开源虚拟试验平台,用于大规模建筑仿真、数据收集、持续控制和实验监测。Sinergym 提供了用于培训和运行控制器的统一界面、预定义基准、实验可视化和复制支持,以及即用型软件库中的全面文档。本文1)与其他现有框架相比,重点介绍了Sinergym的主要特点;2)介绍了其基本用法;3)通过几个具有代表性的例子,展示了其在基于RL的BEO中的适用性。通过整合仿真、数据和控制,Sinergym 可支持开发智能、数据驱动型应用,从而提高楼宇运营的效率和响应速度,这与数字孪生技术的目标不谋而合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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