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Zhelun Chen, Jin Wen, Yicheng Li, L James Lo, Gabriel Grajewski, Vance (Wm.) Payne, Steven T. Bushby, Amanda Pertzborn, Zheng O'Neill, Zhiyao Yang, Caleb Calfa
With buildings contributing significantly to electricity usage, enabling demand flexibility becomes a challenge, especially when accounting for occupant comfort. This study introduces an advanced co-simulation framework that integrated a range of models
In the U.S., commercial buildings are responsible for approximately 36 % of total energy consumption, and the heating, ventilation, and air-conditioning (HVAC) systems make up about 52 % of that total. Improving building operations can significantly reduce
The use of Modelica for simulating the dynamic behaviors of building heating, ventilation, and air conditioning (HVAC) systems has gained popularity. Calibration of a Modelica model that represents large and complex HVAC systems involves the determination
We present the development of a bias compensating reinforcement learning (RL) algorithm that optimizes thermal comfort (by minimizing tracking error) and control utilization (by penalizing setpoint deviations) in a multi-zone heating, ventilation, and air
Zhelun Chen, Yicheng Li, Jin Wen, Liang Chung Lo, Gabriel Grajewski, Zheng O'Neill, Amanda Pertzborn, Vance (Wm.) Payne, Steven T. Bushby
As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to
Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building
Cybersecurity has been a topic of increasing importance to the building services community for several years. While fully securing large and complex building systems can be complicated, some basic precautions can easily be applied to any system, and some
Parastoo Delgoshaei, Mohammad Heidarinejad, Mark Austin
Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for
Cybersecurity has been a topic of increasing importance for several years. While fully securing a large and complex system can be very complicated, there are some basic precautions that can easily be applied to any system, and some basic precautions that
Stephen Zimmerman, Brian Polidoro, Lisa Ng, William Stuart Dols, Steven Emmerich
Virus Particle Exposure in Residences (ViPER) is a web-based, single zone indoor air quality and ventilation analysis tool developed by the National Institute of Standards and Technology (NIST) for evaluating an occupant's relative exposure to virus-laden
A coupled energy, airflow, and contaminant transport building model was developed using co-simulation between EnergyPlus and CONTAM. The model was used to analyze different strategies to control supply air delivery and return air recirculation rates
Customers and transactive energy (TE) market managers may rely on load forecasting algorithms to purchase or sell power in a forward market environment, using day-ahead and real-time pricing structures. Accurate load forecasting becomes necessary when a
The goal of the Embedded Intelligence in Buildings program at the National Institute of Standards and Technology (NIST) is to develop and deploy advances in measurement science that will improve building operations to achieve lower operating costs
Cybersecurity has been a topic of increasing importance for several years. While fully securing a large system can be very complicated, there are some basic precautions that can easily be applied to any system. The purpose of this document is to briefly
Adam J. Kopach, G. F. Nellis, David Bradley, Douglas T. Reindl, Amanda Pertzborn
The Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) will be used to experimentally study control strategies within the realm of commercial building air-conditioning systems. A TRNSYS [1] model of
With the increase of fluctuating renewable resources both on the grid and locally on buildings, a need arises for buildings to be flexible such that its energy demand can be modified to match energy generation. This study aims to characterize key