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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
Piotr A. Domanski, Mark O. McLinden, Valeri I. Babushok, Ian Bell, Tara Fortin, Michael Hegetschweiler, Marcia L. Huber, Mark A. Kedzierski, Dennis Kim, Lingnan Lin, Gregory T. Linteris, Stephanie L. Outcalt, Vance (Wm.) Payne, Richard A. Perkins, Aaron Rowane, Harrison M. Skye
This project addresses the objectives of the Statement of Need number WPSON-17-20 "No/Low Global Warming Potential Alternatives to Ozone Depleting Refrigerants." Its goal was to identify low global-warming-potential (GWP), non-flammable refrigerants to
Niraj Kunwar, Som Shrestha, Andre Desjarlais, Gina Accawi, Lisa Ng, Laverne Dalgleish
Energy consumption in residential buildings is primarily driven by space conditioning applications. Space heating and cooling, on average, consume approximately 50% of the energy in the residential buildings in the U.S. The primary energy use due to
Guowen Li, Yangyang Fu, Amanda Pertzborn, Zheng O'Neill, Jin Wen
Model Predictive Control (MPC) has been demonstrated to be an efficient way to reduce building operating costs, especially for buildings with thermal storage systems, by changing the power demand profiles. Different parameter settings of MPC have also been
Zhelun Chen, Jin Wen, Steven T. Bushby, Caleb Calfa, Yangyang Fu, Gabriel Grajewski, Yicheng Li, L. James Lo, Zheng O'Neill, Vance (Wm.) Payne, Amanda Pertzborn, Zhiyao Yang
The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building
This tutorial is a guide on how to implement the NIST infiltration correlations (Ng et al., 2021) into EnergyPlus building energy simulation software for the US Department of Energy prototype commercial buildings. The implementation can also be generalized
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
Lingnan Lin, Lei Gao, Mark A. Kedzierski, Yunho Hwang
A new neural network architecture, namely DimNet, was designed for correlating dimensionless quantities with power-law-like relations. Unlike common neural networks that are usually used as "black-boxes", DimNet is interpretable as it can be converted to