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Brandon Hencey
Researcher at Air Force Research Laboratory
Publications - 55
Citations - 1722
Brandon Hencey is an academic researcher from Air Force Research Laboratory. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 15, co-authored 53 publications receiving 1598 citations. Previous affiliations of Brandon Hencey include University of Illinois at Urbana–Champaign & Cornell University.
Papers
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Journal ArticleDOI
Model Predictive Control for the Operation of Building Cooling Systems
TL;DR: This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy.
Proceedings ArticleDOI
Model Predictive Control of thermal energy storage in building cooling systems
TL;DR: This paper addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, periodic robust invariant sets as terminal constraints and a moving window blocking strategy.
Proceedings ArticleDOI
Model predictive control for the operation of building cooling systems
TL;DR: In this paper, a model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems, which can achieve reduction in the central plant electricity cost and improvement of its efficiency.
Journal ArticleDOI
Model predictive HVAC control with online occupancy model
Justin R. Dobbs,Brandon Hencey +1 more
TL;DR: An occupancy-predicting control algorithm for heating, ventilation, and air conditioning systems in buildings that incorporates the building's thermal properties, local weather predictions, and a self-tuning stochastic occupancy model to reduce energy consumption while maintaining occupant comfort is presented.
Proceedings ArticleDOI
Online building thermal parameter estimation via Unscented Kalman Filtering
Peter Radecki,Brandon Hencey +1 more
TL;DR: This study demonstrates how an Unscented Kalman Filter augmented for parameter estimation can accurately learn and predict a building's thermal response and proposes a novel gray-box approach based on a multi-zone thermal network and validate it with EnergyPlus simulation data.