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Simulation and optimization of energy efficient operation of HVAC system as demand response with distributed energy resources
2015
2015 Winter Simulation Conference (WSC)
Optimal control of building's HVAC (Heating Ventilation and Air Conditioning) system as a demand response may not only reduce energy cost in buildings, but also reduce energy production in grid, stabilize energy grid and promote smart grid. In this paper, we describe a model predictive control (MPC) framework that optimally determines control profiles of the HVAC system as demand response. A Nonlinear Autoregressive Neural Network (NARNET) is used to model the thermal behavior of the building
doi:10.1109/wsc.2015.7408227
dblp:conf/wsc/LeeHL15
fatcat:hcgdzsb265ahra3xucahetzz4m