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

在美国，建筑物约占全年能源消耗总量的40%，其中约44%用于加热、通风和空调（HVAC）系统。间接地通过他们的能源使用，建筑物贡献了约35%的能源消耗在美国的总二氧化碳排放量，有一个显着的潜力，减少能源使用的建筑物及其相关的环境影响，通过使用先进的控制HVAC系统。此外，美国能源部还启动了一项关于电网互动高效建筑的国家战略，这将有助于将建筑物的能源效率和需求灵活性提高三倍，并提高电网效率和可靠性。模型预测控制（MPC）已经成为一种潜在的先进的楼宇控制技术，以实现这些目标。然而，其向实践的过渡受到根本性挑战的阻碍，包括开发用于控制的精确建筑模型的困难和高成本以及在建筑物中实施MPC的高工程工作。该项目将为克服建筑MPC的这些基本挑战奠定科学基础，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HVAC系统的数据驱动学习和控制的新方法和算法，并在真实的建筑物的实验中进行演示。更广泛地说，这项研究将推进学习和控制复杂物理系统的科学知识，这将在许多其他应用中产生深远的影响。它将把研究工作纳入教育和推广活动，包括为本科生提供新的研究机会，并为K-12学校学生和公众开展推广活动，以丰富公众对建筑节能及其技术的了解。该项目的目标是开发一个新的框架、理论和方法，通过桥接机器学习、动力学、控制和优化，实现建筑HVAC系统的有效和高效的数据驱动建模、学习和控制。为此，该项目的具体目标是开发（1）用于构建HVAC系统的物理信息数据驱动建模方法，该方法有效地将适当的领域见解纳入机器学习模型;（2）主动学习方法，以获得最丰富的实验数据，从而提高模型准确性和样本效率;以及（3）具有物理信息数据驱动模型的基于学习的MPC（LB-MPC）的有效公式和有效优化算法。通过对各种真实的建筑物的大量实验验证，验证了这些方法的可行性和优点。该项目通过建立一个整体的物理信息数据驱动的建模基础和一套用于建筑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，高性能和实用的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见解纳入数据驱动模型，实现可靠，样本效率和准确的数据驱动模型，从而推进面向控制的建筑建模的最新技术水平。它还具有通过主动学习方法转换数据驱动建筑建模的训练数据集合的潜力，这些方法可以找到最佳的激励轨迹进行学习。最后，它将通过制定有效和易于处理的LB-MPC优化问题和定制算法来有效解决这些问题，从而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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.