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.

建筑物约占美国年度能源消耗的40％，其中约44％用于供暖，通风和空调（HVAC）系统。通过其能源使用间接，建筑物约占美国能源消耗的年度二氧化碳排放量的35％，通过使用HVAC系统的高级控制，可以减少建筑物的能源使用及其相关的环境影响。此外，美国能源部已经制定了一项有关网格互动有效建筑物的国家战略，这将有助于建筑物的能源效率和需求灵活性并提高电网效率和可靠性。模型预测控制（MPC）已成为一种潜在的高级建筑控制技术，以实现这些目标。但是，其向实践的过渡受到了根本挑战的阻碍，包括难以开发用于控制的准确建筑模型以及在建筑物中实施MPC的高工程努力的高成本。该项目将奠定科学基础，以克服MPC对建筑物的这些基本挑战，整合机器学习，控制理论，优化理论和建筑科学。它将开发用于数据驱动的HVAC系统学习和控制的新颖方法和算法，并在实现真实建筑物的实验中证明它们。更广泛地说，这项研究将提高对复杂物理系统的学习和控制的科学知识，这将在许多其他应用中产生深远的影响。它将将研究工作纳入教育和宣传，包括本科生的新研究机会以及向K-12学校学生和公众推广活动，以丰富公众对建筑能源效率及其技术的了解。这些努力通过STEM中代表性不足的少数群体的广泛招聘和指导得到补充。该项目的目的是通过弥合机器学习，动态，控制和优化的新框架，理论和方法来开发有效，有效的数据驱动的建模，学习和控制HVAC系统的新框架，理论和方法。为此，该项目的具体目标是开发（1）一种用于构建HVAC系统的物理知识的数据驱动建模方法，该方法有效地将适当的域见解纳入机器学习模型中；（2）积极学习方法，以获取最有用的实验数据，以提高模型的准确性和样本效率；（3）具有物理知识的数据驱动模型的基于学习的MPC（LB-MPC）的有效配方和有效的优化算法。这些方法的可行性和优点将通过对各种实际建筑物的广泛实验验证来验证。该项目通过建立一个整体物理学的数据驱动建模基础，以及用于构建HVAC系统的整体物理学知识的建模基础和优化方法，为建筑物提供了自主，性能和实用的LB-MPC途径。它将通过有效地将适当的域洞察力纳入数据驱动的模型中，使可靠，样本效率和准确的数据驱动模型有效地融合到数据驱动的模型中，从而弥合黑框和灰色框建模方法之间的差距，以在面向控制的建筑物建模上推进最新技术。它还具有通过主动学习方法来转换数据驱动的建筑物建模的培训数据的收集，从而找到最佳的学习轨迹。最后，它将通过制定有效且可拖延的LB-MPC优化问题和调整算法来有效地解决这些问题来克服数据驱动控制的计算挑战。该奖项反映了NSF的法定任务，并认为通过基金会的智力和更广泛的影响，可以通过评估来进行评估，以审查CRITERIA。

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