Journal
APPLIED ENERGY
Volume 87, Issue 10, Pages 3092-3102Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2010.04.008
Keywords
HVAC; Optimization; Data mining; Neural network ensemble; Energy saving; Particle swarm optimization algorithm
Categories
Funding
- Iowa Energy Center [08-01]
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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 set-point 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%. (C) 2010 Elsevier Ltd. All rights reserved.
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