Modeling and optimization of HVAC energy consumption

Andrew Kusiak, Mingyang Li and Fan Tang

Applied Energy, 2010, vol. 87, issue 10, 3092-3102

Abstract: A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%.

Keywords: HVAC; Optimization; Data; mining; Neural; network; ensemble; Energy; saving; Particle; swarm; optimization; algorithm (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (56)

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