ERI: Towards Data-driven Learning and Control of Building HVAC Systems

ERI：迈向数据驱动的建筑 HVAC 系统学习和控制

基本信息

批准号：
2138388
负责人：
Truong Nghiem
金额：
$ 19.95万
依托单位：
Northern Arizona University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138388&HistoricalAwards=false
关键词：
ERI Towards Data driven Learning

项目摘要

Buildings account for about 40% of the total annual energy consumption in the U.S., of which about 44% is for heating, ventilation, and air conditioning (HVAC) systems. Indirectly through their energy use, buildings contribute about 35% of the total annual carbon dioxide emissions from energy consumption in the U.S. There is a significant potential for reducing energy use of buildings and their associated environmental impact by using advanced control of HVAC systems. Moreover, the U.S. Department of Energy has initiated a national strategy on grid-interactive efficient buildings, that will help triple the energy efficiency and demand flexibility of buildings and improve the power grid efficiency and reliability. Model Predictive Control (MPC) has emerged as a potential advanced building control technology to attain these goals. However, its transition to practice has been hampered by fundamental challenges, including the difficulty and high cost of developing accurate building models for control and the high engineering effort to implement MPC in buildings. This project will lay the scientific foundation for overcoming these fundamental challenges of MPC for buildings, integrating machine learning, control theory, optimization theory, and building science. It will develop novel methods and algorithms for data-driven learning and control of HVAC systems, and demonstrate them in experiments with real buildings. More broadly, this research will advance scientific knowledge in learning and control of complex physical systems, which will have far-reaching impacts in many other applications. It will integrate research efforts into education and outreach, including new research opportunities for undergraduate students and outreach activities to K-12 school students and the public to enrich public understanding of building energy efficiency and its technologies. These efforts are complemented by extensive recruitment and mentorship of underrepresented minorities in STEM.The goal of this project is to develop a new framework, theory, and methods for effective and efficient data-driven modeling, learning, and control of building HVAC systems by bridging machine learning, dynamics, control, and optimization. To this end, the specific objectives of this project are to develop (1) a physics-informed data-driven modeling approach for building HVAC systems that effectively incorporates appropriate domain insights into machine learning models; (2) active learning methods to obtain the most informative experimental data for improving model accuracy and sample efficiency; and (3) effective formulations and efficient optimization algorithms for learning-based MPC (LB-MPC) with the physics-informed data-driven models. The feasibility and merits of these methods will be validated through extensive experimental verification on a variety of real buildings. This project provides a path towards autonomous, performant, and practical LB-MPC for buildings by establishing a holistic physics-informed data-driven modeling foundation and a suite of learning, control, and optimization methods for building HVAC systems. It will bridge the gap between black-box and gray-box modeling approaches to advance the state of the art on control-oriented building modeling by effectively incorporating appropriate domain insights into data-driven models, enabling reliable, sample-efficient, and accurate data-driven models. It also has the potential to transform the collection of training data for data-driven building modeling through active learning methods that find the optimal excitation trajectory for learning. Finally, it will overcome the computational challenges of data-driven control by formulating effective and tractable LB-MPC optimization problems and tailoring algorithms for solving these problems efficiently.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑能耗约占美国年度总能耗的 40%，其中约 44% 用于供暖、通风和空调 (HVAC) 系统。在美国，建筑物通过其能源消耗间接产生的二氧化碳排放量约占美国年度能源消耗二氧化碳排放总量的 35%。通过使用 HVAC 系统的先进控制，在减少建筑物能源使用及其相关环境影响方面具有巨大潜力。此外，美国能源部启动了电网互动高效建筑国家战略，将有助于将建筑物的能源效率和需求灵活性提高三倍，提高电网效率和可靠性。模型预测控制 (MPC) 已成为实现这些目标的潜在先进建筑控制技术。然而，其向实践的过渡受到了根本性挑战的阻碍，包括开发用于控制的精确建筑模型的难度和高成本，以及在建筑物中实施 MPC 的大量工程工作。该项目将机器学习、控制理论、优化理论和建筑科学相结合，为克服建筑 MPC 的这些基本挑战奠定科学基础。它将开发用于 HVAC 系统的数据驱动学习和控制的新颖方法和算法，并在真实建筑的实验中进行演示。更广泛地说，这项研究将推进复杂物理系统学习和控制方面的科学知识，这将对许多其他应用产生深远的影响。它将把研究工作融入教育和推广中，包括为本科生提供新的研究机会，以及为 K-12 学校学生和公众开展推广活动，以加深公众对建筑能效及其技术的了解。这些努力得到了对 STEM 中代表性不足的少数群体的广泛招募和指导的补充。该项目的目标是开发一种新的框架、理论和方法，通过桥接构建 HVAC 系统的有效和高效的数据驱动建模、学习和控制机器学习、动力学、控制和优化。为此，该项目的具体目标是开发（1）一种基于物理的数据驱动建模方法，用于构建 HVAC 系统，有效地将适当的领域洞察融入机器学习模型中；（2）主动学习方法，获取最丰富的实验数据，提高模型精度和样本效率；（3）基于物理知识的数据驱动模型的基于学习的 MPC（LB-MPC）的有效公式和高效优化算法。这些方法的可行性和优点将通过对各种真实建筑的广泛实验验证来验证。该项目通过建立一个整体的物理信息数据驱动建模基础和一套用于构建 HVAC 系统的学习、控制和优化方法，为建筑物提供了一条通往自主、高性能和实用 LB-MPC 的道路。它将弥合黑盒和灰盒建模方法之间的差距，通过有效地将适当的领域洞察融入数据驱动模型，从而实现可靠、样本高效且准确的数据，从而推进面向控制的建筑建模的最新技术驱动模型。它还具有通过主动学习方法来改变数据驱动建筑建模的训练数据收集的潜力，这些方法可以找到最佳的学习激励轨迹。最后，它将通过制定有效且易于处理的 LB-MPC 优化问题并定制有效解决这些问题的算法来克服数据驱动控制的计算挑战。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

DOI：
10.23919/acc55779.2023.10155901
发表时间：
2023-05
期刊：
2023 American Control Conference (ACC)
影响因子：
0
作者：
Truong X. Nghiem;Ján Drgoňa;Colin N. Jones;Zoltán Nagy;Roland Schwan;Biswadip Dey;A. Chakrabarty
通讯作者：
Truong X. Nghiem;Ján Drgoňa;Colin N. Jones;Zoltán Nagy;Roland Schwan;Biswadip Dey;A. Chakrabarty

Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems

用于动力系统数据驱动建模的因果深度算子网络