一个综合开发框架,用于数据驱动的模型预测控制建筑物中的热区
Peter Klanatsky1, François Veynandt1, Christian Heschl1
1Hochschule Burgenland University of Applied Sciences, Campus Pinkafeld, Steinamangerstraße 21 7423 Pinkafeld, Austria.
MethodsX
|December 1, 2025
概括
一个新的模拟环境有助于开发数据驱动模型预测控制 (DMPC) 用于建筑能源管理. 这一经验证的框架支持灵活的控制策略,加强可再生能源的电网整合.
科学领域:
- 建筑能源系统 建筑能源系统
- 控制理论 控制理论 控制理论
- 实现可再生能源的整合.
背景情况:
- 越来越多的可再生能源整合需要加强建筑能源的灵活性.
- 数据驱动型预测控制 (DMPC) 是建筑能源管理的一个关键技术.
- 现有的模拟工具可能缺乏灵活性来测试先进的DMPC策略.
研究的目的:
- 引入一个全面的模拟环境,用于开发和评估建筑物DMPC解决方案.
- 提供一个验证的框架来测试先进的控制策略,包括热激活建筑结构 (TABS) 和太阳能遮阳.
- 促进对复杂建筑系统的分散控制方法的研究.
主要方法:
- 开发了一个可定制的区域模型,用于多个房间的配置.
- 集成的DMPC算法与可适应的状态空间模型和强化学习.
- 支持各种优化架构 (集中式,分散式).
- 使用生活实验室办公楼一年多的数据验证了环境.
主要成果:
- 热带模型实现了高精度 (平均绝对误差<0.5°C).
- 模拟环境准确地捕获了TABS和太阳遮阳的动态.
- 在模拟复杂的建筑系统方面表现出强大的性能.
结论:
- 经过验证的模拟环境有效地支持建筑物DMPC的开发.
- 该框架适用于评估先进的控制策略和分散的方法.
- 让研究人员能够应对复杂的建筑能源管理和电网集成方面的挑战.
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